Analysis and Prediction of Covid-19 Cases in India

This project will help to make analysis and to do visualization of data for Covid-19 disease in India as well as among the world. Also it helps to predict the values for Confirmed and deceased patients in future. We have applied machine Learning Alggorithm to predict the values, and also make visualization for those values.

In [1]:
#Importing required libraries
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
In [2]:
#Importing Dataset
df = pd.read_csv('Daily_India_covid_19.csv')
df
Out[2]:
Date Daily Confirmed Daily Deaths Daily Recovered Total Confirmed Total Deaths Total Recovered Total Active
0 30-Jan 1 0 0 1 0 0 1
1 31-Jan 0 0 0 1 0 0 1
2 01-Feb 0 0 0 1 0 0 1
3 02-Feb 1 0 0 2 0 0 2
4 03-Feb 1 0 0 3 0 0 3
... ... ... ... ... ... ... ... ...
111 20-May 5720 134 3113 112200 3435 45422 63343
112 21-May 6023 148 3131 118223 3583 48553 66087
113 22-May 6536 142 3280 124759 3725 51833 69201
114 23-May 6665 142 2576 131422 3867 54409 73146
115 24-May 7111 156 3283 138535 4023 57692 76820

116 rows × 8 columns

In [3]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 116 entries, 0 to 115
Data columns (total 8 columns):
 #   Column           Non-Null Count  Dtype 
---  ------           --------------  ----- 
 0   Date             116 non-null    object
 1   Daily Confirmed  116 non-null    int64 
 2   Daily Deaths     116 non-null    int64 
 3   Daily Recovered  116 non-null    int64 
 4   Total Confirmed  116 non-null    int64 
 5   Total Deaths     116 non-null    int64 
 6   Total Recovered  116 non-null    int64 
 7   Total Active     116 non-null    int64 
dtypes: int64(7), object(1)
memory usage: 7.4+ KB

Data Cleaning and Blending

In [4]:
# Adding year to the Date column
df['Date'] = df['Date'] + '-2020'
In [5]:
pd.set_option('display.max_rows',500)
In [6]:
df
Out[6]:
Date Daily Confirmed Daily Deaths Daily Recovered Total Confirmed Total Deaths Total Recovered Total Active
0 30-Jan-2020 1 0 0 1 0 0 1
1 31-Jan-2020 0 0 0 1 0 0 1
2 01-Feb-2020 0 0 0 1 0 0 1
3 02-Feb-2020 1 0 0 2 0 0 2
4 03-Feb-2020 1 0 0 3 0 0 3
5 04-Feb-2020 0 0 0 3 0 0 3
6 05-Feb-2020 0 0 0 3 0 0 3
7 06-Feb-2020 0 0 0 3 0 0 3
8 07-Feb-2020 0 0 0 3 0 0 3
9 08-Feb-2020 0 0 0 3 0 0 3
10 09-Feb-2020 0 0 0 3 0 0 3
11 10-Feb-2020 0 0 0 3 0 0 3
12 11-Feb-2020 0 0 0 3 0 0 3
13 12-Feb-2020 0 0 0 3 0 0 3
14 13-Feb-2020 0 0 1 3 0 1 2
15 14-Feb-2020 0 0 0 3 0 1 2
16 15-Feb-2020 0 0 0 3 0 1 2
17 16-Feb-2020 0 0 1 3 0 2 1
18 17-Feb-2020 0 0 0 3 0 2 1
19 18-Feb-2020 0 0 0 3 0 2 1
20 19-Feb-2020 0 0 0 3 0 2 1
21 20-Feb-2020 0 0 1 3 0 3 0
22 21-Feb-2020 0 0 0 3 0 3 0
23 22-Feb-2020 0 0 0 3 0 3 0
24 23-Feb-2020 0 0 0 3 0 3 0
25 24-Feb-2020 0 0 0 3 0 3 0
26 25-Feb-2020 0 0 0 3 0 3 0
27 26-Feb-2020 0 0 0 3 0 3 0
28 27-Feb-2020 0 0 0 3 0 3 0
29 28-Feb-2020 0 0 0 3 0 3 0
30 29-Feb-2020 0 0 0 3 0 3 0
31 01-Mar-2020 0 0 0 3 0 3 0
32 02-Mar-2020 2 0 0 5 0 3 2
33 03-Mar-2020 1 0 0 6 0 3 3
34 04-Mar-2020 22 0 0 28 0 3 25
35 05-Mar-2020 2 0 0 30 0 3 27
36 06-Mar-2020 1 0 0 31 0 3 28
37 07-Mar-2020 3 0 0 34 0 3 31
38 08-Mar-2020 5 0 0 39 0 3 36
39 09-Mar-2020 9 0 0 48 0 3 45
40 10-Mar-2020 15 0 1 63 0 4 59
41 11-Mar-2020 8 0 0 71 0 4 67
42 12-Mar-2020 10 1 0 81 1 4 76
43 13-Mar-2020 10 0 6 91 1 10 80
44 14-Mar-2020 11 1 0 102 2 10 90
45 15-Mar-2020 10 0 3 112 2 13 97
46 16-Mar-2020 14 0 1 126 2 14 110
47 17-Mar-2020 20 1 1 146 3 15 128
48 18-Mar-2020 25 0 0 171 3 15 153
49 19-Mar-2020 27 1 5 198 4 20 174
50 20-Mar-2020 58 0 3 256 4 23 229
51 21-Mar-2020 78 0 0 334 4 23 307
52 22-Mar-2020 69 3 0 403 7 23 373
53 23-Mar-2020 94 2 2 497 9 25 463
54 24-Mar-2020 74 1 15 571 10 40 521
55 25-Mar-2020 86 1 3 657 11 43 603
56 26-Mar-2020 73 5 7 730 16 50 664
57 27-Mar-2020 153 3 25 883 19 75 789
58 28-Mar-2020 136 5 10 1019 24 85 910
59 29-Mar-2020 120 3 17 1139 27 102 1010
60 30-Mar-2020 187 14 35 1326 41 137 1148
61 31-Mar-2020 309 6 13 1635 47 150 1438
62 01-Apr-2020 424 6 19 2059 53 169 1837
63 02-Apr-2020 486 16 22 2545 69 191 2285
64 03-Apr-2020 560 14 39 3105 83 230 2792
65 04-Apr-2020 579 13 56 3684 96 286 3302
66 05-Apr-2020 609 22 43 4293 118 329 3846
67 06-Apr-2020 484 16 65 4777 134 394 4249
68 07-Apr-2020 573 27 75 5350 161 469 4720
69 08-Apr-2020 565 20 96 5915 181 565 5169
70 09-Apr-2020 813 46 70 6728 227 635 5866
71 10-Apr-2020 871 22 151 7599 249 786 6564
72 11-Apr-2020 854 41 186 8453 290 972 7191
73 12-Apr-2020 758 42 114 9211 332 1086 7793
74 13-Apr-2020 1243 27 112 10454 359 1198 8897
75 14-Apr-2020 1031 37 167 11485 396 1365 9724
76 15-Apr-2020 886 27 144 12371 423 1509 10439
77 16-Apr-2020 1061 26 258 13432 449 1767 11216
78 17-Apr-2020 922 38 273 14354 487 2040 11827
79 18-Apr-2020 1371 35 426 15725 522 2466 12737
80 19-Apr-2020 1580 38 388 17305 560 2854 13891
81 20-Apr-2020 1239 33 419 18544 593 3273 14678
82 21-Apr-2020 1537 53 703 20081 646 3976 15459
83 22-Apr-2020 1292 36 394 21373 682 4370 16321
84 23-Apr-2020 1667 40 642 23040 722 5012 17306
85 24-Apr-2020 1408 59 484 24448 781 5496 18171
86 25-Apr-2020 1835 44 442 26283 825 5938 19520
87 26-Apr-2020 1607 56 585 27890 881 6523 20486
88 27-Apr-2020 1568 58 580 29458 939 7103 21416
89 28-Apr-2020 1902 69 636 31360 1008 7739 22613
90 29-Apr-2020 1705 71 690 33065 1079 8429 23557
91 30-Apr-2020 1801 75 630 34866 1154 9059 24653
92 01-May-2020 2396 77 962 37262 1231 10021 26010
93 02-May-2020 2564 92 831 39826 1323 10852 27651
94 03-May-2020 2952 140 911 42778 1463 11763 29552
95 04-May-2020 3656 103 1082 46434 1566 12845 32023
96 05-May-2020 2971 128 1295 49405 1694 14140 33571
97 06-May-2020 3602 91 1161 53007 1785 15301 35921
98 07-May-2020 3344 104 1475 56351 1889 16776 37686
99 08-May-2020 3339 97 1111 59690 1986 17887 39817
100 09-May-2020 3175 115 1414 62865 2101 19301 41463
101 10-May-2020 4311 112 1669 67176 2213 20970 43993
102 11-May-2020 3592 81 1579 70768 2294 22549 45925
103 12-May-2020 3562 120 1905 74330 2414 24454 47462
104 13-May-2020 3726 137 1963 78056 2551 26417 49088
105 14-May-2020 3991 97 1594 82047 2648 28011 51388
106 15-May-2020 3808 104 2234 85855 2752 30245 52858
107 16-May-2020 4794 120 4012 90649 2872 34257 53520
108 17-May-2020 5049 152 2538 95698 3024 36795 55879
109 18-May-2020 4628 131 2482 100326 3155 39277 57894
110 19-May-2020 6154 146 3032 106480 3301 42309 60870
111 20-May-2020 5720 134 3113 112200 3435 45422 63343
112 21-May-2020 6023 148 3131 118223 3583 48553 66087
113 22-May-2020 6536 142 3280 124759 3725 51833 69201
114 23-May-2020 6665 142 2576 131422 3867 54409 73146
115 24-May-2020 7111 156 3283 138535 4023 57692 76820
In [7]:
# Changing the datatype of Date from object to datetime
df['Date'] = pd.to_datetime(df['Date'])
df['Date']
Out[7]:
0     2020-01-30
1     2020-01-31
2     2020-02-01
3     2020-02-02
4     2020-02-03
5     2020-02-04
6     2020-02-05
7     2020-02-06
8     2020-02-07
9     2020-02-08
10    2020-02-09
11    2020-02-10
12    2020-02-11
13    2020-02-12
14    2020-02-13
15    2020-02-14
16    2020-02-15
17    2020-02-16
18    2020-02-17
19    2020-02-18
20    2020-02-19
21    2020-02-20
22    2020-02-21
23    2020-02-22
24    2020-02-23
25    2020-02-24
26    2020-02-25
27    2020-02-26
28    2020-02-27
29    2020-02-28
30    2020-02-29
31    2020-03-01
32    2020-03-02
33    2020-03-03
34    2020-03-04
35    2020-03-05
36    2020-03-06
37    2020-03-07
38    2020-03-08
39    2020-03-09
40    2020-03-10
41    2020-03-11
42    2020-03-12
43    2020-03-13
44    2020-03-14
45    2020-03-15
46    2020-03-16
47    2020-03-17
48    2020-03-18
49    2020-03-19
50    2020-03-20
51    2020-03-21
52    2020-03-22
53    2020-03-23
54    2020-03-24
55    2020-03-25
56    2020-03-26
57    2020-03-27
58    2020-03-28
59    2020-03-29
60    2020-03-30
61    2020-03-31
62    2020-04-01
63    2020-04-02
64    2020-04-03
65    2020-04-04
66    2020-04-05
67    2020-04-06
68    2020-04-07
69    2020-04-08
70    2020-04-09
71    2020-04-10
72    2020-04-11
73    2020-04-12
74    2020-04-13
75    2020-04-14
76    2020-04-15
77    2020-04-16
78    2020-04-17
79    2020-04-18
80    2020-04-19
81    2020-04-20
82    2020-04-21
83    2020-04-22
84    2020-04-23
85    2020-04-24
86    2020-04-25
87    2020-04-26
88    2020-04-27
89    2020-04-28
90    2020-04-29
91    2020-04-30
92    2020-05-01
93    2020-05-02
94    2020-05-03
95    2020-05-04
96    2020-05-05
97    2020-05-06
98    2020-05-07
99    2020-05-08
100   2020-05-09
101   2020-05-10
102   2020-05-11
103   2020-05-12
104   2020-05-13
105   2020-05-14
106   2020-05-15
107   2020-05-16
108   2020-05-17
109   2020-05-18
110   2020-05-19
111   2020-05-20
112   2020-05-21
113   2020-05-22
114   2020-05-23
115   2020-05-24
Name: Date, dtype: datetime64[ns]
In [8]:
df
Out[8]:
Date Daily Confirmed Daily Deaths Daily Recovered Total Confirmed Total Deaths Total Recovered Total Active
0 2020-01-30 1 0 0 1 0 0 1
1 2020-01-31 0 0 0 1 0 0 1
2 2020-02-01 0 0 0 1 0 0 1
3 2020-02-02 1 0 0 2 0 0 2
4 2020-02-03 1 0 0 3 0 0 3
5 2020-02-04 0 0 0 3 0 0 3
6 2020-02-05 0 0 0 3 0 0 3
7 2020-02-06 0 0 0 3 0 0 3
8 2020-02-07 0 0 0 3 0 0 3
9 2020-02-08 0 0 0 3 0 0 3
10 2020-02-09 0 0 0 3 0 0 3
11 2020-02-10 0 0 0 3 0 0 3
12 2020-02-11 0 0 0 3 0 0 3
13 2020-02-12 0 0 0 3 0 0 3
14 2020-02-13 0 0 1 3 0 1 2
15 2020-02-14 0 0 0 3 0 1 2
16 2020-02-15 0 0 0 3 0 1 2
17 2020-02-16 0 0 1 3 0 2 1
18 2020-02-17 0 0 0 3 0 2 1
19 2020-02-18 0 0 0 3 0 2 1
20 2020-02-19 0 0 0 3 0 2 1
21 2020-02-20 0 0 1 3 0 3 0
22 2020-02-21 0 0 0 3 0 3 0
23 2020-02-22 0 0 0 3 0 3 0
24 2020-02-23 0 0 0 3 0 3 0
25 2020-02-24 0 0 0 3 0 3 0
26 2020-02-25 0 0 0 3 0 3 0
27 2020-02-26 0 0 0 3 0 3 0
28 2020-02-27 0 0 0 3 0 3 0
29 2020-02-28 0 0 0 3 0 3 0
30 2020-02-29 0 0 0 3 0 3 0
31 2020-03-01 0 0 0 3 0 3 0
32 2020-03-02 2 0 0 5 0 3 2
33 2020-03-03 1 0 0 6 0 3 3
34 2020-03-04 22 0 0 28 0 3 25
35 2020-03-05 2 0 0 30 0 3 27
36 2020-03-06 1 0 0 31 0 3 28
37 2020-03-07 3 0 0 34 0 3 31
38 2020-03-08 5 0 0 39 0 3 36
39 2020-03-09 9 0 0 48 0 3 45
40 2020-03-10 15 0 1 63 0 4 59
41 2020-03-11 8 0 0 71 0 4 67
42 2020-03-12 10 1 0 81 1 4 76
43 2020-03-13 10 0 6 91 1 10 80
44 2020-03-14 11 1 0 102 2 10 90
45 2020-03-15 10 0 3 112 2 13 97
46 2020-03-16 14 0 1 126 2 14 110
47 2020-03-17 20 1 1 146 3 15 128
48 2020-03-18 25 0 0 171 3 15 153
49 2020-03-19 27 1 5 198 4 20 174
50 2020-03-20 58 0 3 256 4 23 229
51 2020-03-21 78 0 0 334 4 23 307
52 2020-03-22 69 3 0 403 7 23 373
53 2020-03-23 94 2 2 497 9 25 463
54 2020-03-24 74 1 15 571 10 40 521
55 2020-03-25 86 1 3 657 11 43 603
56 2020-03-26 73 5 7 730 16 50 664
57 2020-03-27 153 3 25 883 19 75 789
58 2020-03-28 136 5 10 1019 24 85 910
59 2020-03-29 120 3 17 1139 27 102 1010
60 2020-03-30 187 14 35 1326 41 137 1148
61 2020-03-31 309 6 13 1635 47 150 1438
62 2020-04-01 424 6 19 2059 53 169 1837
63 2020-04-02 486 16 22 2545 69 191 2285
64 2020-04-03 560 14 39 3105 83 230 2792
65 2020-04-04 579 13 56 3684 96 286 3302
66 2020-04-05 609 22 43 4293 118 329 3846
67 2020-04-06 484 16 65 4777 134 394 4249
68 2020-04-07 573 27 75 5350 161 469 4720
69 2020-04-08 565 20 96 5915 181 565 5169
70 2020-04-09 813 46 70 6728 227 635 5866
71 2020-04-10 871 22 151 7599 249 786 6564
72 2020-04-11 854 41 186 8453 290 972 7191
73 2020-04-12 758 42 114 9211 332 1086 7793
74 2020-04-13 1243 27 112 10454 359 1198 8897
75 2020-04-14 1031 37 167 11485 396 1365 9724
76 2020-04-15 886 27 144 12371 423 1509 10439
77 2020-04-16 1061 26 258 13432 449 1767 11216
78 2020-04-17 922 38 273 14354 487 2040 11827
79 2020-04-18 1371 35 426 15725 522 2466 12737
80 2020-04-19 1580 38 388 17305 560 2854 13891
81 2020-04-20 1239 33 419 18544 593 3273 14678
82 2020-04-21 1537 53 703 20081 646 3976 15459
83 2020-04-22 1292 36 394 21373 682 4370 16321
84 2020-04-23 1667 40 642 23040 722 5012 17306
85 2020-04-24 1408 59 484 24448 781 5496 18171
86 2020-04-25 1835 44 442 26283 825 5938 19520
87 2020-04-26 1607 56 585 27890 881 6523 20486
88 2020-04-27 1568 58 580 29458 939 7103 21416
89 2020-04-28 1902 69 636 31360 1008 7739 22613
90 2020-04-29 1705 71 690 33065 1079 8429 23557
91 2020-04-30 1801 75 630 34866 1154 9059 24653
92 2020-05-01 2396 77 962 37262 1231 10021 26010
93 2020-05-02 2564 92 831 39826 1323 10852 27651
94 2020-05-03 2952 140 911 42778 1463 11763 29552
95 2020-05-04 3656 103 1082 46434 1566 12845 32023
96 2020-05-05 2971 128 1295 49405 1694 14140 33571
97 2020-05-06 3602 91 1161 53007 1785 15301 35921
98 2020-05-07 3344 104 1475 56351 1889 16776 37686
99 2020-05-08 3339 97 1111 59690 1986 17887 39817
100 2020-05-09 3175 115 1414 62865 2101 19301 41463
101 2020-05-10 4311 112 1669 67176 2213 20970 43993
102 2020-05-11 3592 81 1579 70768 2294 22549 45925
103 2020-05-12 3562 120 1905 74330 2414 24454 47462
104 2020-05-13 3726 137 1963 78056 2551 26417 49088
105 2020-05-14 3991 97 1594 82047 2648 28011 51388
106 2020-05-15 3808 104 2234 85855 2752 30245 52858
107 2020-05-16 4794 120 4012 90649 2872 34257 53520
108 2020-05-17 5049 152 2538 95698 3024 36795 55879
109 2020-05-18 4628 131 2482 100326 3155 39277 57894
110 2020-05-19 6154 146 3032 106480 3301 42309 60870
111 2020-05-20 5720 134 3113 112200 3435 45422 63343
112 2020-05-21 6023 148 3131 118223 3583 48553 66087
113 2020-05-22 6536 142 3280 124759 3725 51833 69201
114 2020-05-23 6665 142 2576 131422 3867 54409 73146
115 2020-05-24 7111 156 3283 138535 4023 57692 76820

Data Visualization

In [9]:
fig = px.bar(df, x="Date", y="Total Confirmed", color="Total Confirmed",
                  hover_data=['Total Confirmed'],
                 color_discrete_sequence = px.colors.sequential.Plasma_r)
fig.update_layout(title_text='Trend of Daily Coronavirus Cases in India',
                  plot_bgcolor='rgb(275, 270, 273)',width=800, height=600)
fig.show()
In [10]:
import plotly.graph_objs as go
fig = go.Figure(data=[
go.Bar(name='Confirmed', x=df['Date'], y=df['Total Confirmed'],marker_color='#FF0000'),
go.Bar(name='Recovered', x=df['Date'], y=df['Total Recovered'],marker_color='#2fcc41')])
fig.update_layout(barmode='stack',width=1000, height=600)
fig.update_traces(textposition='inside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.update_layout(title_text='Number of people Confirmed and Recovered among them',
                  plot_bgcolor='rgb(275, 270, 273)')
fig.update_layout(plot_bgcolor='rgb(275, 270, 273)',yaxis_title='Total_Confirmed',xaxis_title='Date')
fig.show()
In [11]:
import plotly.graph_objs as go
fig = go.Figure(data=[
go.Bar(name='Confirmed', x=df['Date'], y=df['Total Confirmed'],marker_color='#FF0000'),
go.Bar(name='Deaths', x=df['Date'], y=df['Total Deaths'],marker_color='Blue')])
fig.update_layout(barmode='stack',width=1000, height=600)
fig.update_traces(textposition='inside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.update_layout(title_text='Number of people Confirmed and Deceased among them',
                  plot_bgcolor='rgb(275, 270, 273)')
fig.update_layout(plot_bgcolor='rgb(275, 270, 273)',yaxis_title='Total_Deaths',xaxis_title='Date')
fig.show()
In [12]:
# Splitting of the Date column
def add_features(df):
    df['Year'] = df['Date'].dt.year
    df['Month'] = df['Date'].dt.month
    df['Week'] = df['Date'].dt.week
    df['Day'] = df['Date'].dt.day
In [13]:
add_features(df)
In [14]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 116 entries, 0 to 115
Data columns (total 12 columns):
 #   Column           Non-Null Count  Dtype         
---  ------           --------------  -----         
 0   Date             116 non-null    datetime64[ns]
 1   Daily Confirmed  116 non-null    int64         
 2   Daily Deaths     116 non-null    int64         
 3   Daily Recovered  116 non-null    int64         
 4   Total Confirmed  116 non-null    int64         
 5   Total Deaths     116 non-null    int64         
 6   Total Recovered  116 non-null    int64         
 7   Total Active     116 non-null    int64         
 8   Year             116 non-null    int64         
 9   Month            116 non-null    int64         
 10  Week             116 non-null    int64         
 11  Day              116 non-null    int64         
dtypes: datetime64[ns](1), int64(11)
memory usage: 11.0 KB
In [15]:
df
Out[15]:
Date Daily Confirmed Daily Deaths Daily Recovered Total Confirmed Total Deaths Total Recovered Total Active Year Month Week Day
0 2020-01-30 1 0 0 1 0 0 1 2020 1 5 30
1 2020-01-31 0 0 0 1 0 0 1 2020 1 5 31
2 2020-02-01 0 0 0 1 0 0 1 2020 2 5 1
3 2020-02-02 1 0 0 2 0 0 2 2020 2 5 2
4 2020-02-03 1 0 0 3 0 0 3 2020 2 6 3
5 2020-02-04 0 0 0 3 0 0 3 2020 2 6 4
6 2020-02-05 0 0 0 3 0 0 3 2020 2 6 5
7 2020-02-06 0 0 0 3 0 0 3 2020 2 6 6
8 2020-02-07 0 0 0 3 0 0 3 2020 2 6 7
9 2020-02-08 0 0 0 3 0 0 3 2020 2 6 8
10 2020-02-09 0 0 0 3 0 0 3 2020 2 6 9
11 2020-02-10 0 0 0 3 0 0 3 2020 2 7 10
12 2020-02-11 0 0 0 3 0 0 3 2020 2 7 11
13 2020-02-12 0 0 0 3 0 0 3 2020 2 7 12
14 2020-02-13 0 0 1 3 0 1 2 2020 2 7 13
15 2020-02-14 0 0 0 3 0 1 2 2020 2 7 14
16 2020-02-15 0 0 0 3 0 1 2 2020 2 7 15
17 2020-02-16 0 0 1 3 0 2 1 2020 2 7 16
18 2020-02-17 0 0 0 3 0 2 1 2020 2 8 17
19 2020-02-18 0 0 0 3 0 2 1 2020 2 8 18
20 2020-02-19 0 0 0 3 0 2 1 2020 2 8 19
21 2020-02-20 0 0 1 3 0 3 0 2020 2 8 20
22 2020-02-21 0 0 0 3 0 3 0 2020 2 8 21
23 2020-02-22 0 0 0 3 0 3 0 2020 2 8 22
24 2020-02-23 0 0 0 3 0 3 0 2020 2 8 23
25 2020-02-24 0 0 0 3 0 3 0 2020 2 9 24
26 2020-02-25 0 0 0 3 0 3 0 2020 2 9 25
27 2020-02-26 0 0 0 3 0 3 0 2020 2 9 26
28 2020-02-27 0 0 0 3 0 3 0 2020 2 9 27
29 2020-02-28 0 0 0 3 0 3 0 2020 2 9 28
30 2020-02-29 0 0 0 3 0 3 0 2020 2 9 29
31 2020-03-01 0 0 0 3 0 3 0 2020 3 9 1
32 2020-03-02 2 0 0 5 0 3 2 2020 3 10 2
33 2020-03-03 1 0 0 6 0 3 3 2020 3 10 3
34 2020-03-04 22 0 0 28 0 3 25 2020 3 10 4
35 2020-03-05 2 0 0 30 0 3 27 2020 3 10 5
36 2020-03-06 1 0 0 31 0 3 28 2020 3 10 6
37 2020-03-07 3 0 0 34 0 3 31 2020 3 10 7
38 2020-03-08 5 0 0 39 0 3 36 2020 3 10 8
39 2020-03-09 9 0 0 48 0 3 45 2020 3 11 9
40 2020-03-10 15 0 1 63 0 4 59 2020 3 11 10
41 2020-03-11 8 0 0 71 0 4 67 2020 3 11 11
42 2020-03-12 10 1 0 81 1 4 76 2020 3 11 12
43 2020-03-13 10 0 6 91 1 10 80 2020 3 11 13
44 2020-03-14 11 1 0 102 2 10 90 2020 3 11 14
45 2020-03-15 10 0 3 112 2 13 97 2020 3 11 15
46 2020-03-16 14 0 1 126 2 14 110 2020 3 12 16
47 2020-03-17 20 1 1 146 3 15 128 2020 3 12 17
48 2020-03-18 25 0 0 171 3 15 153 2020 3 12 18
49 2020-03-19 27 1 5 198 4 20 174 2020 3 12 19
50 2020-03-20 58 0 3 256 4 23 229 2020 3 12 20
51 2020-03-21 78 0 0 334 4 23 307 2020 3 12 21
52 2020-03-22 69 3 0 403 7 23 373 2020 3 12 22
53 2020-03-23 94 2 2 497 9 25 463 2020 3 13 23
54 2020-03-24 74 1 15 571 10 40 521 2020 3 13 24
55 2020-03-25 86 1 3 657 11 43 603 2020 3 13 25
56 2020-03-26 73 5 7 730 16 50 664 2020 3 13 26
57 2020-03-27 153 3 25 883 19 75 789 2020 3 13 27
58 2020-03-28 136 5 10 1019 24 85 910 2020 3 13 28
59 2020-03-29 120 3 17 1139 27 102 1010 2020 3 13 29
60 2020-03-30 187 14 35 1326 41 137 1148 2020 3 14 30
61 2020-03-31 309 6 13 1635 47 150 1438 2020 3 14 31
62 2020-04-01 424 6 19 2059 53 169 1837 2020 4 14 1
63 2020-04-02 486 16 22 2545 69 191 2285 2020 4 14 2
64 2020-04-03 560 14 39 3105 83 230 2792 2020 4 14 3
65 2020-04-04 579 13 56 3684 96 286 3302 2020 4 14 4
66 2020-04-05 609 22 43 4293 118 329 3846 2020 4 14 5
67 2020-04-06 484 16 65 4777 134 394 4249 2020 4 15 6
68 2020-04-07 573 27 75 5350 161 469 4720 2020 4 15 7
69 2020-04-08 565 20 96 5915 181 565 5169 2020 4 15 8
70 2020-04-09 813 46 70 6728 227 635 5866 2020 4 15 9
71 2020-04-10 871 22 151 7599 249 786 6564 2020 4 15 10
72 2020-04-11 854 41 186 8453 290 972 7191 2020 4 15 11
73 2020-04-12 758 42 114 9211 332 1086 7793 2020 4 15 12
74 2020-04-13 1243 27 112 10454 359 1198 8897 2020 4 16 13
75 2020-04-14 1031 37 167 11485 396 1365 9724 2020 4 16 14
76 2020-04-15 886 27 144 12371 423 1509 10439 2020 4 16 15
77 2020-04-16 1061 26 258 13432 449 1767 11216 2020 4 16 16
78 2020-04-17 922 38 273 14354 487 2040 11827 2020 4 16 17
79 2020-04-18 1371 35 426 15725 522 2466 12737 2020 4 16 18
80 2020-04-19 1580 38 388 17305 560 2854 13891 2020 4 16 19
81 2020-04-20 1239 33 419 18544 593 3273 14678 2020 4 17 20
82 2020-04-21 1537 53 703 20081 646 3976 15459 2020 4 17 21
83 2020-04-22 1292 36 394 21373 682 4370 16321 2020 4 17 22
84 2020-04-23 1667 40 642 23040 722 5012 17306 2020 4 17 23
85 2020-04-24 1408 59 484 24448 781 5496 18171 2020 4 17 24
86 2020-04-25 1835 44 442 26283 825 5938 19520 2020 4 17 25
87 2020-04-26 1607 56 585 27890 881 6523 20486 2020 4 17 26
88 2020-04-27 1568 58 580 29458 939 7103 21416 2020 4 18 27
89 2020-04-28 1902 69 636 31360 1008 7739 22613 2020 4 18 28
90 2020-04-29 1705 71 690 33065 1079 8429 23557 2020 4 18 29
91 2020-04-30 1801 75 630 34866 1154 9059 24653 2020 4 18 30
92 2020-05-01 2396 77 962 37262 1231 10021 26010 2020 5 18 1
93 2020-05-02 2564 92 831 39826 1323 10852 27651 2020 5 18 2
94 2020-05-03 2952 140 911 42778 1463 11763 29552 2020 5 18 3
95 2020-05-04 3656 103 1082 46434 1566 12845 32023 2020 5 19 4
96 2020-05-05 2971 128 1295 49405 1694 14140 33571 2020 5 19 5
97 2020-05-06 3602 91 1161 53007 1785 15301 35921 2020 5 19 6
98 2020-05-07 3344 104 1475 56351 1889 16776 37686 2020 5 19 7
99 2020-05-08 3339 97 1111 59690 1986 17887 39817 2020 5 19 8
100 2020-05-09 3175 115 1414 62865 2101 19301 41463 2020 5 19 9
101 2020-05-10 4311 112 1669 67176 2213 20970 43993 2020 5 19 10
102 2020-05-11 3592 81 1579 70768 2294 22549 45925 2020 5 20 11
103 2020-05-12 3562 120 1905 74330 2414 24454 47462 2020 5 20 12
104 2020-05-13 3726 137 1963 78056 2551 26417 49088 2020 5 20 13
105 2020-05-14 3991 97 1594 82047 2648 28011 51388 2020 5 20 14
106 2020-05-15 3808 104 2234 85855 2752 30245 52858 2020 5 20 15
107 2020-05-16 4794 120 4012 90649 2872 34257 53520 2020 5 20 16
108 2020-05-17 5049 152 2538 95698 3024 36795 55879 2020 5 20 17
109 2020-05-18 4628 131 2482 100326 3155 39277 57894 2020 5 21 18
110 2020-05-19 6154 146 3032 106480 3301 42309 60870 2020 5 21 19
111 2020-05-20 5720 134 3113 112200 3435 45422 63343 2020 5 21 20
112 2020-05-21 6023 148 3131 118223 3583 48553 66087 2020 5 21 21
113 2020-05-22 6536 142 3280 124759 3725 51833 69201 2020 5 21 22
114 2020-05-23 6665 142 2576 131422 3867 54409 73146 2020 5 21 23
115 2020-05-24 7111 156 3283 138535 4023 57692 76820 2020 5 21 24
In [16]:
# Slicing of the data
data = df[62:]
data
Out[16]:
Date Daily Confirmed Daily Deaths Daily Recovered Total Confirmed Total Deaths Total Recovered Total Active Year Month Week Day
62 2020-04-01 424 6 19 2059 53 169 1837 2020 4 14 1
63 2020-04-02 486 16 22 2545 69 191 2285 2020 4 14 2
64 2020-04-03 560 14 39 3105 83 230 2792 2020 4 14 3
65 2020-04-04 579 13 56 3684 96 286 3302 2020 4 14 4
66 2020-04-05 609 22 43 4293 118 329 3846 2020 4 14 5
67 2020-04-06 484 16 65 4777 134 394 4249 2020 4 15 6
68 2020-04-07 573 27 75 5350 161 469 4720 2020 4 15 7
69 2020-04-08 565 20 96 5915 181 565 5169 2020 4 15 8
70 2020-04-09 813 46 70 6728 227 635 5866 2020 4 15 9
71 2020-04-10 871 22 151 7599 249 786 6564 2020 4 15 10
72 2020-04-11 854 41 186 8453 290 972 7191 2020 4 15 11
73 2020-04-12 758 42 114 9211 332 1086 7793 2020 4 15 12
74 2020-04-13 1243 27 112 10454 359 1198 8897 2020 4 16 13
75 2020-04-14 1031 37 167 11485 396 1365 9724 2020 4 16 14
76 2020-04-15 886 27 144 12371 423 1509 10439 2020 4 16 15
77 2020-04-16 1061 26 258 13432 449 1767 11216 2020 4 16 16
78 2020-04-17 922 38 273 14354 487 2040 11827 2020 4 16 17
79 2020-04-18 1371 35 426 15725 522 2466 12737 2020 4 16 18
80 2020-04-19 1580 38 388 17305 560 2854 13891 2020 4 16 19
81 2020-04-20 1239 33 419 18544 593 3273 14678 2020 4 17 20
82 2020-04-21 1537 53 703 20081 646 3976 15459 2020 4 17 21
83 2020-04-22 1292 36 394 21373 682 4370 16321 2020 4 17 22
84 2020-04-23 1667 40 642 23040 722 5012 17306 2020 4 17 23
85 2020-04-24 1408 59 484 24448 781 5496 18171 2020 4 17 24
86 2020-04-25 1835 44 442 26283 825 5938 19520 2020 4 17 25
87 2020-04-26 1607 56 585 27890 881 6523 20486 2020 4 17 26
88 2020-04-27 1568 58 580 29458 939 7103 21416 2020 4 18 27
89 2020-04-28 1902 69 636 31360 1008 7739 22613 2020 4 18 28
90 2020-04-29 1705 71 690 33065 1079 8429 23557 2020 4 18 29
91 2020-04-30 1801 75 630 34866 1154 9059 24653 2020 4 18 30
92 2020-05-01 2396 77 962 37262 1231 10021 26010 2020 5 18 1
93 2020-05-02 2564 92 831 39826 1323 10852 27651 2020 5 18 2
94 2020-05-03 2952 140 911 42778 1463 11763 29552 2020 5 18 3
95 2020-05-04 3656 103 1082 46434 1566 12845 32023 2020 5 19 4
96 2020-05-05 2971 128 1295 49405 1694 14140 33571 2020 5 19 5
97 2020-05-06 3602 91 1161 53007 1785 15301 35921 2020 5 19 6
98 2020-05-07 3344 104 1475 56351 1889 16776 37686 2020 5 19 7
99 2020-05-08 3339 97 1111 59690 1986 17887 39817 2020 5 19 8
100 2020-05-09 3175 115 1414 62865 2101 19301 41463 2020 5 19 9
101 2020-05-10 4311 112 1669 67176 2213 20970 43993 2020 5 19 10
102 2020-05-11 3592 81 1579 70768 2294 22549 45925 2020 5 20 11
103 2020-05-12 3562 120 1905 74330 2414 24454 47462 2020 5 20 12
104 2020-05-13 3726 137 1963 78056 2551 26417 49088 2020 5 20 13
105 2020-05-14 3991 97 1594 82047 2648 28011 51388 2020 5 20 14
106 2020-05-15 3808 104 2234 85855 2752 30245 52858 2020 5 20 15
107 2020-05-16 4794 120 4012 90649 2872 34257 53520 2020 5 20 16
108 2020-05-17 5049 152 2538 95698 3024 36795 55879 2020 5 20 17
109 2020-05-18 4628 131 2482 100326 3155 39277 57894 2020 5 21 18
110 2020-05-19 6154 146 3032 106480 3301 42309 60870 2020 5 21 19
111 2020-05-20 5720 134 3113 112200 3435 45422 63343 2020 5 21 20
112 2020-05-21 6023 148 3131 118223 3583 48553 66087 2020 5 21 21
113 2020-05-22 6536 142 3280 124759 3725 51833 69201 2020 5 21 22
114 2020-05-23 6665 142 2576 131422 3867 54409 73146 2020 5 21 23
115 2020-05-24 7111 156 3283 138535 4023 57692 76820 2020 5 21 24
In [17]:
d = data[['Day']]
w = data[['Week']]
m = data[['Month']]
y = data[['Total Confirmed']]
In [18]:
plt.plot(d,y, label=r'$Confirmed Cases$', color='red')
plt.gca().update(dict(title='Day vs Confirmed_Cases', xlabel='Day', ylabel='Total Confirmed'))
plt.legend()
plt.show()
In [19]:
plt.plot(w,y, label=r'$Confirmed Cases$', color='green')
plt.gca().update(dict(title='Week vs Confirmed_Cases', xlabel='Week', ylabel='Total Confirmed'))
plt.legend()
plt.show()
In [20]:
plt.plot(m,y, label=r'$Confirmed Cases$', color='blue')
plt.gca().update(dict(title='Month vs Confirmed_Cases', xlabel='Month', ylabel='Total Confirmed'))
plt.legend()
plt.show()
In [21]:
#Importing train test split model for Confirmed cases per Week
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(w, y, train_size= 0.9)
In [22]:
x_train
Out[22]:
Week
99 19
88 18
91 18
87 17
111 21
67 15
114 21
75 16
97 19
98 19
66 14
79 16
93 18
84 17
105 20
81 17
80 16
92 18
70 15
90 18
112 21
78 16
115 21
102 20
106 20
113 21
71 15
89 18
94 18
95 19
77 16
76 16
104 20
86 17
107 20
103 20
63 14
69 15
74 16
65 14
108 20
68 15
83 17
64 14
72 15
82 17
62 14
109 21
In [23]:
y_train
Out[23]:
Total Confirmed
99 59690
88 29458
91 34866
87 27890
111 112200
67 4777
114 131422
75 11485
97 53007
98 56351
66 4293
79 15725
93 39826
84 23040
105 82047
81 18544
80 17305
92 37262
70 6728
90 33065
112 118223
78 14354
115 138535
102 70768
106 85855
113 124759
71 7599
89 31360
94 42778
95 46434
77 13432
76 12371
104 78056
86 26283
107 90649
103 74330
63 2545
69 5915
74 10454
65 3684
108 95698
68 5350
83 21373
64 3105
72 8453
82 20081
62 2059
109 100326
In [24]:
#Importing and Fitting Machine Learning Algorithm 
from sklearn.linear_model import LinearRegression
model = LinearRegression()
model.fit(x_train,y_train)
Out[24]:
LinearRegression()
In [25]:
#Predicting Values
model.predict([[24],[27],[30],[35],[40]])
Out[25]:
array([[148204.11342672],
       [197171.73385719],
       [246139.35428767],
       [327752.05500512],
       [409364.75572258]])
In [26]:
predict = model.predict(x_train)
predict
Out[26]:
array([[ 66591.41270926],
       [ 50268.87256577],
       [ 50268.87256577],
       [ 33946.33242228],
       [ 99236.49299624],
       [  1301.25213529],
       [ 99236.49299624],
       [ 17623.79227878],
       [ 66591.41270926],
       [ 66591.41270926],
       [-15021.2880082 ],
       [ 17623.79227878],
       [ 50268.87256577],
       [ 33946.33242228],
       [ 82913.95285275],
       [ 33946.33242228],
       [ 17623.79227878],
       [ 50268.87256577],
       [  1301.25213529],
       [ 50268.87256577],
       [ 99236.49299624],
       [ 17623.79227878],
       [ 99236.49299624],
       [ 82913.95285275],
       [ 82913.95285275],
       [ 99236.49299624],
       [  1301.25213529],
       [ 50268.87256577],
       [ 50268.87256577],
       [ 66591.41270926],
       [ 17623.79227878],
       [ 17623.79227878],
       [ 82913.95285275],
       [ 33946.33242228],
       [ 82913.95285275],
       [ 82913.95285275],
       [-15021.2880082 ],
       [  1301.25213529],
       [ 17623.79227878],
       [-15021.2880082 ],
       [ 82913.95285275],
       [  1301.25213529],
       [ 33946.33242228],
       [-15021.2880082 ],
       [  1301.25213529],
       [ 33946.33242228],
       [-15021.2880082 ],
       [ 99236.49299624]])
In [27]:
#Comparing Predicted and Actual Values
In [28]:
plt.scatter(x_train, predict, color='red', marker='*', label=r'$Predicted Values$')
plt.scatter(x_train, y_train, color='green', marker='o', label=r'$Actual Values$')
plt.gca().update(dict(title='Predicted_values vs Actual_Values', xlabel='Day', ylabel='Total Confirmed'))
plt.legend()
plt.show()
In [29]:
#Accuracy of the Model
model.score(x_train,y_train)*100
Out[29]:
87.3480748721295

Importing Prophet Model

In [30]:
#Importing Prophet
from fbprophet import Prophet
In [31]:
test = data[['Date', 'Total Confirmed']]
In [32]:
test
Out[32]:
Date Total Confirmed
62 2020-04-01 2059
63 2020-04-02 2545
64 2020-04-03 3105
65 2020-04-04 3684
66 2020-04-05 4293
67 2020-04-06 4777
68 2020-04-07 5350
69 2020-04-08 5915
70 2020-04-09 6728
71 2020-04-10 7599
72 2020-04-11 8453
73 2020-04-12 9211
74 2020-04-13 10454
75 2020-04-14 11485
76 2020-04-15 12371
77 2020-04-16 13432
78 2020-04-17 14354
79 2020-04-18 15725
80 2020-04-19 17305
81 2020-04-20 18544
82 2020-04-21 20081
83 2020-04-22 21373
84 2020-04-23 23040
85 2020-04-24 24448
86 2020-04-25 26283
87 2020-04-26 27890
88 2020-04-27 29458
89 2020-04-28 31360
90 2020-04-29 33065
91 2020-04-30 34866
92 2020-05-01 37262
93 2020-05-02 39826
94 2020-05-03 42778
95 2020-05-04 46434
96 2020-05-05 49405
97 2020-05-06 53007
98 2020-05-07 56351
99 2020-05-08 59690
100 2020-05-09 62865
101 2020-05-10 67176
102 2020-05-11 70768
103 2020-05-12 74330
104 2020-05-13 78056
105 2020-05-14 82047
106 2020-05-15 85855
107 2020-05-16 90649
108 2020-05-17 95698
109 2020-05-18 100326
110 2020-05-19 106480
111 2020-05-20 112200
112 2020-05-21 118223
113 2020-05-22 124759
114 2020-05-23 131422
115 2020-05-24 138535
In [33]:
# Changing the column names
test.columns = ['ds','y']
In [34]:
test.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 54 entries, 62 to 115
Data columns (total 2 columns):
 #   Column  Non-Null Count  Dtype         
---  ------  --------------  -----         
 0   ds      54 non-null     datetime64[ns]
 1   y       54 non-null     int64         
dtypes: datetime64[ns](1), int64(1)
memory usage: 996.0 bytes
In [35]:
#Fitting Prophet model
m = Prophet()
m.fit(test)
INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
Out[35]:
<fbprophet.forecaster.Prophet at 0xf890a08>
In [36]:
# Creating future values
future = m.make_future_dataframe(periods=90,freq='D')
future
Out[36]:
ds
0 2020-04-01
1 2020-04-02
2 2020-04-03
3 2020-04-04
4 2020-04-05
5 2020-04-06
6 2020-04-07
7 2020-04-08
8 2020-04-09
9 2020-04-10
10 2020-04-11
11 2020-04-12
12 2020-04-13
13 2020-04-14
14 2020-04-15
15 2020-04-16
16 2020-04-17
17 2020-04-18
18 2020-04-19
19 2020-04-20
20 2020-04-21
21 2020-04-22
22 2020-04-23
23 2020-04-24
24 2020-04-25
25 2020-04-26
26 2020-04-27
27 2020-04-28
28 2020-04-29
29 2020-04-30
30 2020-05-01
31 2020-05-02
32 2020-05-03
33 2020-05-04
34 2020-05-05
35 2020-05-06
36 2020-05-07
37 2020-05-08
38 2020-05-09
39 2020-05-10
40 2020-05-11
41 2020-05-12
42 2020-05-13
43 2020-05-14
44 2020-05-15
45 2020-05-16
46 2020-05-17
47 2020-05-18
48 2020-05-19
49 2020-05-20
50 2020-05-21
51 2020-05-22
52 2020-05-23
53 2020-05-24
54 2020-05-25
55 2020-05-26
56 2020-05-27
57 2020-05-28
58 2020-05-29
59 2020-05-30
60 2020-05-31
61 2020-06-01
62 2020-06-02
63 2020-06-03
64 2020-06-04
65 2020-06-05
66 2020-06-06
67 2020-06-07
68 2020-06-08
69 2020-06-09
70 2020-06-10
71 2020-06-11
72 2020-06-12
73 2020-06-13
74 2020-06-14
75 2020-06-15
76 2020-06-16
77 2020-06-17
78 2020-06-18
79 2020-06-19
80 2020-06-20
81 2020-06-21
82 2020-06-22
83 2020-06-23
84 2020-06-24
85 2020-06-25
86 2020-06-26
87 2020-06-27
88 2020-06-28
89 2020-06-29
90 2020-06-30
91 2020-07-01
92 2020-07-02
93 2020-07-03
94 2020-07-04
95 2020-07-05
96 2020-07-06
97 2020-07-07
98 2020-07-08
99 2020-07-09
100 2020-07-10
101 2020-07-11
102 2020-07-12
103 2020-07-13
104 2020-07-14
105 2020-07-15
106 2020-07-16
107 2020-07-17
108 2020-07-18
109 2020-07-19
110 2020-07-20
111 2020-07-21
112 2020-07-22
113 2020-07-23
114 2020-07-24
115 2020-07-25
116 2020-07-26
117 2020-07-27
118 2020-07-28
119 2020-07-29
120 2020-07-30
121 2020-07-31
122 2020-08-01
123 2020-08-02
124 2020-08-03
125 2020-08-04
126 2020-08-05
127 2020-08-06
128 2020-08-07
129 2020-08-08
130 2020-08-09
131 2020-08-10
132 2020-08-11
133 2020-08-12
134 2020-08-13
135 2020-08-14
136 2020-08-15
137 2020-08-16
138 2020-08-17
139 2020-08-18
140 2020-08-19
141 2020-08-20
142 2020-08-21
143 2020-08-22
In [37]:
len(test)
Out[37]:
54
In [38]:
len(future)
Out[38]:
144
In [39]:
#Predicting Future Values
forecast = m.predict(future)
forecast
Out[39]:
ds trend yhat_lower yhat_upper trend_lower trend_upper additive_terms additive_terms_lower additive_terms_upper weekly weekly_lower weekly_upper multiplicative_terms multiplicative_terms_lower multiplicative_terms_upper yhat
0 2020-04-01 1562.478044 440.134391 2705.934471 1562.478044 1562.478044 35.330152 35.330152 35.330152 35.330152 35.330152 35.330152 0.0 0.0 0.0 1597.808197
1 2020-04-02 2237.589341 1034.642193 3424.697178 2237.589341 2237.589341 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 0.0 0.0 0.0 2215.132558
2 2020-04-03 2912.700637 1593.587836 3999.316225 2912.700637 2912.700637 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 0.0 0.0 0.0 2850.321864
3 2020-04-04 3587.811933 2448.872845 4824.079486 3587.811933 3587.811933 61.822304 61.822304 61.822304 61.822304 61.822304 61.822304 0.0 0.0 0.0 3649.634237
4 2020-04-05 4262.923246 3282.597276 5717.911227 4262.923246 4262.923246 319.238479 319.238479 319.238479 319.238479 319.238479 319.238479 0.0 0.0 0.0 4582.161725
5 2020-04-06 4938.034559 3610.116626 5875.782594 4938.034559 4938.034559 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 0.0 0.0 0.0 4715.881648
6 2020-04-07 5613.557237 4332.059007 6617.467544 5613.557237 5613.557237 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 0.0 0.0 0.0 5504.154769
7 2020-04-08 6289.079916 5121.991510 7475.741192 6289.079916 6289.079916 35.330152 35.330152 35.330152 35.330152 35.330152 35.330152 0.0 0.0 0.0 6324.410068
8 2020-04-09 6964.888527 5764.247428 8058.687867 6964.888527 6964.888527 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 0.0 0.0 0.0 6942.431744
9 2020-04-10 7641.105899 6419.106369 8709.616752 7641.105899 7641.105899 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 0.0 0.0 0.0 7578.727125
10 2020-04-11 8317.323271 7271.478616 9472.300589 8317.323271 8317.323271 61.822304 61.822304 61.822304 61.822304 61.822304 61.822304 0.0 0.0 0.0 8379.145575
11 2020-04-12 9350.423222 8523.346750 10775.219594 9350.423222 9350.423222 319.238479 319.238479 319.238479 319.238479 319.238479 319.238479 0.0 0.0 0.0 9669.661701
12 2020-04-13 10383.523173 9059.127535 11271.647286 10383.523173 10383.523173 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 0.0 0.0 0.0 10161.370262
13 2020-04-14 11420.715229 10128.493831 12404.474412 11420.715229 11420.715229 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 0.0 0.0 0.0 11311.312761
14 2020-04-15 12459.491372 11376.004560 13725.012122 12459.491372 12459.491372 35.330152 35.330152 35.330152 35.330152 35.330152 35.330152 0.0 0.0 0.0 12494.821524
15 2020-04-16 13498.267514 12290.895406 14639.515867 13498.267514 13498.267514 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 0.0 0.0 0.0 13475.810731
16 2020-04-17 14539.482482 13298.366420 15615.946024 14539.482482 14539.482482 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 0.0 0.0 0.0 14477.103708
17 2020-04-18 15580.697450 14511.271692 16841.877709 15580.697450 15580.697450 61.822304 61.822304 61.822304 61.822304 61.822304 61.822304 0.0 0.0 0.0 15642.519754
18 2020-04-19 17074.725041 16201.063865 18472.646654 17074.725041 17074.725041 319.238479 319.238479 319.238479 319.238479 319.238479 319.238479 0.0 0.0 0.0 17393.963521
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111 2020-07-21 444776.535287 385517.915238 504785.693662 385584.299858 504497.052243 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 0.0 0.0 0.0 444667.132819
112 2020-07-22 450118.333277 388871.804832 511748.572767 389405.768689 511259.541004 35.330152 35.330152 35.330152 35.330152 35.330152 35.330152 0.0 0.0 0.0 450153.663430
113 2020-07-23 455460.131268 393345.143974 518362.270241 393234.778136 518567.208320 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 0.0 0.0 0.0 455437.674485
114 2020-07-24 460801.929259 396309.083492 526530.140740 396639.889702 525915.346000 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 0.0 0.0 0.0 460739.550485
115 2020-07-25 466143.727250 400090.593839 533052.499856 399645.779707 533222.088606 61.822304 61.822304 61.822304 61.822304 61.822304 61.822304 0.0 0.0 0.0 466205.549554
116 2020-07-26 471485.525240 404242.216051 541117.739535 403179.982170 540468.937714 319.238479 319.238479 319.238479 319.238479 319.238479 319.238479 0.0 0.0 0.0 471804.763720
117 2020-07-27 476827.323231 406251.310414 547263.381340 405785.787022 547858.322647 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 0.0 0.0 0.0 476605.170320
118 2020-07-28 482169.121222 408320.535371 554624.248012 408810.846803 555000.715053 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 0.0 0.0 0.0 482059.718754
119 2020-07-29 487510.919213 412760.648468 561330.471883 413018.700029 561520.610515 35.330152 35.330152 35.330152 35.330152 35.330152 35.330152 0.0 0.0 0.0 487546.249365
120 2020-07-30 492852.717204 415654.332870 568785.045544 416606.931050 568523.122524 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 0.0 0.0 0.0 492830.260421
121 2020-07-31 498194.515194 420454.322198 575224.408685 420410.446443 576286.896165 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 0.0 0.0 0.0 498132.136420
122 2020-08-01 503536.313185 423990.499109 583472.812854 423474.062572 583517.930343 61.822304 61.822304 61.822304 61.822304 61.822304 61.822304 0.0 0.0 0.0 503598.135489
123 2020-08-02 508878.111176 427710.261822 590179.280639 426880.083705 589546.259190 319.238479 319.238479 319.238479 319.238479 319.238479 319.238479 0.0 0.0 0.0 509197.349655
124 2020-08-03 514219.909167 429818.513571 596802.158543 429548.237829 597121.105000 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 0.0 0.0 0.0 513997.756256
125 2020-08-04 519561.707157 432294.680528 604228.745002 433063.203140 605115.145228 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 0.0 0.0 0.0 519452.304689
126 2020-08-05 524903.505148 435909.640884 612432.818395 435984.147391 612031.192403 35.330152 35.330152 35.330152 35.330152 35.330152 35.330152 0.0 0.0 0.0 524938.835300
127 2020-08-06 530245.303139 440161.059044 619354.005783 438958.829135 619415.172563 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 0.0 0.0 0.0 530222.846356
128 2020-08-07 535587.101130 443689.648156 626241.717644 443904.203494 627152.008311 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 0.0 0.0 0.0 535524.722356
129 2020-08-08 540928.899120 446991.035146 634490.475828 447138.519762 634335.294673 61.822304 61.822304 61.822304 61.822304 61.822304 61.822304 0.0 0.0 0.0 540990.721425
130 2020-08-09 546270.697111 450669.151720 640936.158640 450325.304923 639952.325673 319.238479 319.238479 319.238479 319.238479 319.238479 319.238479 0.0 0.0 0.0 546589.935590
131 2020-08-10 551612.495102 454677.371220 647995.759576 453984.105336 646904.680074 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 0.0 0.0 0.0 551390.342191
132 2020-08-11 556954.293093 457636.567552 654722.409537 457250.668188 653853.469521 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 0.0 0.0 0.0 556844.890625
133 2020-08-12 562296.091083 460969.507798 661292.777720 460958.918879 660792.329198 35.330152 35.330152 35.330152 35.330152 35.330152 35.330152 0.0 0.0 0.0 562331.421236
134 2020-08-13 567637.889074 464670.664558 669053.621540 464797.069250 667665.723294 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 0.0 0.0 0.0 567615.432291
135 2020-08-14 572979.687065 467403.054504 675041.959218 467664.278889 674686.063687 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 0.0 0.0 0.0 572917.308291
136 2020-08-15 578321.485056 471343.443313 682796.567403 471193.345634 682333.820162 61.822304 61.822304 61.822304 61.822304 61.822304 61.822304 0.0 0.0 0.0 578383.307360
137 2020-08-16 583663.283046 473923.257430 689429.276571 473786.191408 689630.577336 319.238479 319.238479 319.238479 319.238479 319.238479 319.238479 0.0 0.0 0.0 583982.521526
138 2020-08-17 589005.081037 478242.712917 696218.368778 477249.500351 696766.913558 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 -222.152911 0.0 0.0 0.0 588782.928126
139 2020-08-18 594346.879028 481229.338261 703612.900872 480851.534754 703840.923099 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 -109.402468 0.0 0.0 0.0 594237.476560
140 2020-08-19 599688.677019 484723.590247 711071.956839 484484.949419 711176.963003 35.330152 35.330152 35.330152 35.330152 35.330152 35.330152 0.0 0.0 0.0 599724.007171
141 2020-08-20 605030.475010 487489.770446 718056.484471 488153.515186 718088.471704 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 -22.456783 0.0 0.0 0.0 605008.018227
142 2020-08-21 610372.273000 491742.387481 725863.670759 490996.917300 725416.597515 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 -62.378774 0.0 0.0 0.0 610309.894226
143 2020-08-22 615714.070991 494496.693145 734202.709035 494824.258263 733072.279946 61.822304 61.822304 61.822304 61.822304 61.822304 61.822304 0.0 0.0 0.0 615775.893295
In [ ]:
 
In [40]:
forecast.columns
Out[40]:
Index(['ds', 'trend', 'yhat_lower', 'yhat_upper', 'trend_lower', 'trend_upper',
       'additive_terms', 'additive_terms_lower', 'additive_terms_upper',
       'weekly', 'weekly_lower', 'weekly_upper', 'multiplicative_terms',
       'multiplicative_terms_lower', 'multiplicative_terms_upper', 'yhat'],
      dtype='object')
In [41]:
# Predicted Values for future
forecast[['ds', 'trend', 'yhat_lower', 'yhat_upper', 'yhat']]
Out[41]:
ds trend yhat_lower yhat_upper yhat
0 2020-04-01 1562.478044 440.134391 2705.934471 1597.808197
1 2020-04-02 2237.589341 1034.642193 3424.697178 2215.132558
2 2020-04-03 2912.700637 1593.587836 3999.316225 2850.321864
3 2020-04-04 3587.811933 2448.872845 4824.079486 3649.634237
4 2020-04-05 4262.923246 3282.597276 5717.911227 4582.161725
5 2020-04-06 4938.034559 3610.116626 5875.782594 4715.881648
6 2020-04-07 5613.557237 4332.059007 6617.467544 5504.154769
7 2020-04-08 6289.079916 5121.991510 7475.741192 6324.410068
8 2020-04-09 6964.888527 5764.247428 8058.687867 6942.431744
9 2020-04-10 7641.105899 6419.106369 8709.616752 7578.727125
10 2020-04-11 8317.323271 7271.478616 9472.300589 8379.145575
11 2020-04-12 9350.423222 8523.346750 10775.219594 9669.661701
12 2020-04-13 10383.523173 9059.127535 11271.647286 10161.370262
13 2020-04-14 11420.715229 10128.493831 12404.474412 11311.312761
14 2020-04-15 12459.491372 11376.004560 13725.012122 12494.821524
15 2020-04-16 13498.267514 12290.895406 14639.515867 13475.810731
16 2020-04-17 14539.482482 13298.366420 15615.946024 14477.103708
17 2020-04-18 15580.697450 14511.271692 16841.877709 15642.519754
18 2020-04-19 17074.725041 16201.063865 18472.646654 17393.963521
19 2020-04-20 18569.805190 17225.317921 19569.585432 18347.652279
20 2020-04-21 20064.885339 18732.222164 21123.151314 19955.482870
21 2020-04-22 21560.224801 20426.871059 22664.249893 21595.554953
22 2020-04-23 23055.564263 21888.210244 24201.274641 23033.107481
23 2020-04-24 24552.411091 23408.020268 25720.421802 24490.032317
24 2020-04-25 26049.257918 24896.855584 27295.947092 26111.080222
25 2020-04-26 27814.690142 26998.889314 29301.840256 28133.928621
26 2020-04-27 29580.695641 28205.591442 30551.669636 29358.542730
27 2020-04-28 31346.701140 30067.097644 32412.997284 31237.298672
28 2020-04-29 33113.325235 32019.270242 34302.767216 33148.655388
29 2020-04-30 34879.949331 33742.390857 36025.080427 34857.492548
30 2020-05-01 37153.300539 35923.091799 38272.404192 37090.921765
31 2020-05-02 40108.669027 39107.265683 41441.144751 40170.491331
32 2020-05-03 43064.037515 42317.075542 44578.058716 43383.275994
33 2020-05-04 46376.153248 44962.868596 47353.848413 46154.000337
34 2020-05-05 49688.268980 48444.117785 50649.532012 49578.866512
35 2020-05-06 53001.237170 51820.343181 54171.963036 53036.567322
36 2020-05-07 56314.270957 55234.699638 57402.993430 56291.814175
37 2020-05-08 59627.304745 58411.461735 60700.770653 59564.925971
38 2020-05-09 62940.338565 61778.952642 64102.832707 63002.160870
39 2020-05-10 66253.372386 65374.837665 67686.540616 66572.610865
40 2020-05-11 69566.406218 68197.174461 70472.763237 69344.253307
41 2020-05-12 72879.440071 71608.416637 73905.171848 72770.037603
42 2020-05-13 76192.473924 75076.169259 77362.875862 76227.804076
43 2020-05-14 81534.271915 80327.909869 82613.086202 81511.815132
44 2020-05-15 86876.069906 85765.343004 87843.251095 86813.691132
45 2020-05-16 92217.867896 91141.214712 93381.722915 92279.690201
46 2020-05-17 97559.665887 96698.804978 99057.519539 97878.904367
47 2020-05-18 102901.463878 101467.879787 103771.762262 102679.310967
48 2020-05-19 108243.261869 107012.301142 109269.511667 108133.859401
49 2020-05-20 113585.059859 112575.776950 114805.285674 113620.390012
50 2020-05-21 118926.857850 117676.952834 120088.135413 118904.401067
51 2020-05-22 124268.655841 123058.293191 125361.670745 124206.277067
52 2020-05-23 129610.453832 128474.002813 130878.098212 129672.276136
53 2020-05-24 134952.251823 134040.065671 136373.079329 135271.490302
54 2020-05-25 140294.049813 138834.800789 141206.219064 140071.896902
55 2020-05-26 145635.847804 144332.255357 146784.975278 145526.445336
56 2020-05-27 150977.645795 149695.997101 152300.431377 151012.975947
57 2020-05-28 156319.443786 154726.107375 157759.669729 156296.987003
58 2020-05-29 161661.241776 159798.336317 163415.065260 161598.863002
59 2020-05-30 167003.039767 164900.636692 168938.399581 167064.862071
60 2020-05-31 172344.837758 169954.023235 175097.897972 172664.076237
61 2020-06-01 177686.635749 174364.053397 180249.856038 177464.482838
62 2020-06-02 183028.433739 179136.459892 186381.581877 182919.031271
63 2020-06-03 188370.231730 184105.292071 192265.986806 188405.561882
64 2020-06-04 193712.029721 188742.230797 198289.932625 193689.572938
65 2020-06-05 199053.827712 193359.065854 204065.962930 198991.448938
66 2020-06-06 204395.625702 198191.660663 210352.396191 204457.448007
67 2020-06-07 209737.423693 203112.309328 216791.735624 210056.662172
68 2020-06-08 215079.221684 207313.820199 222245.863465 214857.068773
69 2020-06-09 220421.019675 211676.990344 228580.825435 220311.617207
70 2020-06-10 225762.817665 216772.320285 235021.840862 225798.147818
71 2020-06-11 231104.615656 221193.811300 240941.529820 231082.158873
72 2020-06-12 236446.413647 225396.034728 247282.971939 236384.034873
73 2020-06-13 241788.211638 230001.430264 253894.376628 241850.033942
74 2020-06-14 247130.009629 234908.101070 260550.491259 247449.248108
75 2020-06-15 252471.807619 238774.520813 266144.206152 252249.654708
76 2020-06-16 257813.605610 243083.598379 272263.139253 257704.203142
77 2020-06-17 263155.403601 247950.194877 278853.422423 263190.733753
78 2020-06-18 268497.201592 251929.981926 284658.199534 268474.744809
79 2020-06-19 273838.999582 256068.388171 291051.162361 273776.620808
80 2020-06-20 279180.797573 260255.471779 298124.645436 279242.619877
81 2020-06-21 284522.595564 264949.933831 304250.664411 284841.834043
82 2020-06-22 289864.393555 268200.598124 310034.725701 289642.240644
83 2020-06-23 295206.191545 273114.052177 316149.014826 295096.789077
84 2020-06-24 300547.989536 277393.710867 323248.677369 300583.319688
85 2020-06-25 305889.787527 281775.432075 329728.601047 305867.330744
86 2020-06-26 311231.585518 285845.406937 336141.774449 311169.206744
87 2020-06-27 316573.383508 290196.368269 342729.917248 316635.205813
88 2020-06-28 321915.181499 295226.082647 349181.491201 322234.419978
89 2020-06-29 327256.979490 297938.378231 354818.488081 327034.826579
90 2020-06-30 332598.777481 302665.794443 362216.539980 332489.375013
91 2020-07-01 337940.575471 307154.440032 368533.463540 337975.905624
92 2020-07-02 343282.373462 310469.244568 374779.996800 343259.916679
93 2020-07-03 348624.171453 314390.389501 381729.826374 348561.792679
94 2020-07-04 353965.969444 318007.920114 388725.138101 354027.791748
95 2020-07-05 359307.767434 322730.421822 395716.021872 359627.005914
96 2020-07-06 364649.565425 325908.688693 401849.106220 364427.412514
97 2020-07-07 369991.363416 330321.348559 408884.701987 369881.960948
98 2020-07-08 375333.161407 334391.498173 415319.993385 375368.491559
99 2020-07-09 380674.959398 338575.623560 422458.895341 380652.502615
100 2020-07-10 386016.757388 342806.631575 429505.407082 385954.378614
101 2020-07-11 391358.555379 347592.406139 436903.793843 391420.377683
102 2020-07-12 396700.353370 350873.159068 443262.863755 397019.591849
103 2020-07-13 402042.151361 354110.369105 450218.733937 401819.998450
104 2020-07-14 407383.949351 358715.241542 456294.525478 407274.546883
105 2020-07-15 412725.747342 362924.354884 463762.548022 412761.077494
106 2020-07-16 418067.545333 367125.791351 470003.918844 418045.088550
107 2020-07-17 423409.343324 371256.560499 477219.652589 423346.964550
108 2020-07-18 428751.141314 374521.132797 483957.415499 428812.963619
109 2020-07-19 434092.939305 378124.567718 491051.135208 434412.177784
110 2020-07-20 439434.737296 381608.662638 497575.119169 439212.584385
111 2020-07-21 444776.535287 385517.915238 504785.693662 444667.132819
112 2020-07-22 450118.333277 388871.804832 511748.572767 450153.663430
113 2020-07-23 455460.131268 393345.143974 518362.270241 455437.674485
114 2020-07-24 460801.929259 396309.083492 526530.140740 460739.550485
115 2020-07-25 466143.727250 400090.593839 533052.499856 466205.549554
116 2020-07-26 471485.525240 404242.216051 541117.739535 471804.763720
117 2020-07-27 476827.323231 406251.310414 547263.381340 476605.170320
118 2020-07-28 482169.121222 408320.535371 554624.248012 482059.718754
119 2020-07-29 487510.919213 412760.648468 561330.471883 487546.249365
120 2020-07-30 492852.717204 415654.332870 568785.045544 492830.260421
121 2020-07-31 498194.515194 420454.322198 575224.408685 498132.136420
122 2020-08-01 503536.313185 423990.499109 583472.812854 503598.135489
123 2020-08-02 508878.111176 427710.261822 590179.280639 509197.349655
124 2020-08-03 514219.909167 429818.513571 596802.158543 513997.756256
125 2020-08-04 519561.707157 432294.680528 604228.745002 519452.304689
126 2020-08-05 524903.505148 435909.640884 612432.818395 524938.835300
127 2020-08-06 530245.303139 440161.059044 619354.005783 530222.846356
128 2020-08-07 535587.101130 443689.648156 626241.717644 535524.722356
129 2020-08-08 540928.899120 446991.035146 634490.475828 540990.721425
130 2020-08-09 546270.697111 450669.151720 640936.158640 546589.935590
131 2020-08-10 551612.495102 454677.371220 647995.759576 551390.342191
132 2020-08-11 556954.293093 457636.567552 654722.409537 556844.890625
133 2020-08-12 562296.091083 460969.507798 661292.777720 562331.421236
134 2020-08-13 567637.889074 464670.664558 669053.621540 567615.432291
135 2020-08-14 572979.687065 467403.054504 675041.959218 572917.308291
136 2020-08-15 578321.485056 471343.443313 682796.567403 578383.307360
137 2020-08-16 583663.283046 473923.257430 689429.276571 583982.521526
138 2020-08-17 589005.081037 478242.712917 696218.368778 588782.928126
139 2020-08-18 594346.879028 481229.338261 703612.900872 594237.476560
140 2020-08-19 599688.677019 484723.590247 711071.956839 599724.007171
141 2020-08-20 605030.475010 487489.770446 718056.484471 605008.018227
142 2020-08-21 610372.273000 491742.387481 725863.670759 610309.894226
143 2020-08-22 615714.070991 494496.693145 734202.709035 615775.893295
In [42]:
# Plotting forecast graph between ds and y
m.plot(forecast);
In [43]:
# Plotting forecast graph between ds and yhat
forecast.plot(x='ds', y='yhat', color='red');
In [44]:
# Plotting graphs "ds vs trend" and "Day of week vs weekly values" 
m.plot_components(forecast);

Analysis of Covid-19 for different States in India

In [45]:
df1 = pd.read_csv('Total_India_covid-19.csv')
df1
Out[45]:
State Statecode Confirmed Active Recovered Deaths Last Updated Latitude Longitude
0 Maharashtra MH 50231 33996 14600 1635 24-05-2020 22:25 19.7515 75.7139
1 Tamil Nadu TN 16277 7841 8324 112 24-05-2020 19:21 11.1271 78.6569
2 Gujarat GJ 14063 6793 6412 858 24-05-2020 20:44 22.2587 71.1924
3 Delhi DL 13418 6617 6540 261 24-05-2020 14:29 28.7041 77.1025
4 Rajasthan RJ 7100 3081 3856 163 25-05-2020 10:07 27.0238 74.2179
5 Madhya Pradesh MP 6665 2967 3408 290 24-05-2020 21:44 22.9734 78.6569
6 Uttar Pradesh UP 6268 2569 3538 161 24-05-2020 23:33 26.8467 80.9462
7 West Bengal WB 3667 2056 1339 272 24-05-2020 20:47 22.9868 87.8550
8 Andhra Pradesh AP 2780 883 1841 56 24-05-2020 15:22 15.9129 79.7400
9 State Unassigned UN 2642 2642 0 0 25-05-2020 09:33 NaN NaN
10 Bihar BR 2574 1861 702 11 24-05-2020 23:43 25.0961 85.3131
11 Karnataka KA 2089 1391 654 42 24-05-2020 18:23 15.3173 75.7139
12 Punjab PB 2060 122 1898 40 24-05-2020 22:45 31.1471 75.3412
13 Telangana TG 1854 709 1092 53 24-05-2020 21:22 18.1124 79.0193
14 Jammu and Kashmir JK 1621 791 809 21 24-05-2020 21:11 33.7782 76.5762
15 Odisha OR 1336 779 550 7 24-05-2020 15:32 20.9517 85.0985
16 Haryana HR 1184 403 765 16 24-05-2020 22:13 29.0588 76.0856
17 Kerala KL 848 322 520 6 24-05-2020 20:01 10.8505 76.2711
18 Assam AS 428 363 58 4 25-05-2020 10:21 26.2006 92.9376
19 Jharkhand JH 370 218 148 4 24-05-2020 22:45 23.6102 85.2799
20 Uttarakhand UT 317 255 58 3 24-05-2020 20:44 30.0668 79.0193
21 Chandigarh CH 262 79 179 4 24-05-2020 23:20 30.7333 76.7794
22 Chhattisgarh CT 252 188 64 0 24-05-2020 23:33 21.2787 81.8661
23 Himachal Pradesh HP 203 137 59 4 24-05-2020 22:23 31.1048 77.1734
24 Tripura TR 194 29 165 0 24-05-2020 23:43 23.9408 91.9882
25 Goa GA 66 50 16 0 24-05-2020 11:47 15.2993 74.1240
26 Ladakh LA 52 9 43 0 24-05-2020 14:52 34.2996 78.2932
27 Puducherry PY 41 29 12 0 24-05-2020 23:01 11.9416 79.8083
28 Andaman and Nicobar Islands AN 33 0 33 0 07-05-2020 22:24 11.7401 92.6586
29 Manipur MN 32 30 2 0 24-05-2020 15:00 24.6637 93.9063
30 Meghalaya ML 14 1 12 1 20-05-2020 22:48 25.4670 91.3662
31 Dadra and Nagar Haveli and Daman and Diu DN 2 1 1 0 23-05-2020 23:47 20.2762 73.0083
32 Arunachal Pradesh AR 2 1 1 0 24-05-2020 23:43 28.2180 94.7278
33 Mizoram MZ 1 0 1 0 05-05-2020 22:32 23.1645 92.9376
34 Sikkim SK 1 1 0 0 23-05-2020 19:42 27.3389 88.6065
35 Nagaland NL 0 0 0 0 20-04-2020 08:45 26.1584 94.5624
36 Lakshadweep LD 0 0 0 0 26-03-2020 07:19 10.0760 73.6303
In [46]:
df1.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 37 entries, 0 to 36
Data columns (total 9 columns):
 #   Column        Non-Null Count  Dtype  
---  ------        --------------  -----  
 0   State         37 non-null     object 
 1   Statecode     37 non-null     object 
 2   Confirmed     37 non-null     int64  
 3   Active        37 non-null     int64  
 4   Recovered     37 non-null     int64  
 5   Deaths        37 non-null     int64  
 6   Last Updated  37 non-null     object 
 7   Latitude      36 non-null     float64
 8   Longitude     36 non-null     float64
dtypes: float64(2), int64(4), object(3)
memory usage: 2.7+ KB
In [47]:
import plotly.graph_objs as go
fig = go.Figure(data=[
go.Bar(name='Confirmed', x=df1['Statecode'], y=df1['Confirmed'],marker_color='#FF0000'),
go.Bar(name='Recovered', x=df1['Statecode'], y=df1['Recovered'],marker_color='#2fcc41')])
fig.update_layout(barmode='stack',width=1000, height=600)
fig.update_traces(textposition='inside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.update_layout(title_text='Statewise Number of people Confirmed and Recovered among them',
                  plot_bgcolor='rgb(275, 270, 273)')
fig.update_layout(plot_bgcolor='rgb(275, 270, 273)',yaxis_title='Total_Confirmed',xaxis_title='State')
fig.show()
In [48]:
import plotly.graph_objs as go
fig = go.Figure(data=[
go.Bar(name='Confirmed', x=df1['Statecode'], y=df1['Confirmed'],marker_color='#FF0000'),
go.Bar(name='Deaths', x=df1['Statecode'], y=df1['Deaths'],marker_color='Blue')])
fig.update_layout(barmode='stack',width=1000, height=600)
fig.update_traces(textposition='inside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.update_layout(title_text='Statewise Number of people Confirmed and Deceased among them',
                  plot_bgcolor='rgb(275, 270, 273)')
fig.update_layout(plot_bgcolor='rgb(275, 270, 273)',yaxis_title='Total_Confirmed',xaxis_title='State')
fig.show()
In [49]:
px.pie(df1, values='Confirmed', names='State', title='Statewise Number of people Confirmed')
In [50]:
fig = go.Figure(data=[go.Pie(labels=df1.State, values=df1.Deaths, hole=.3)])
fig.update_layout(title_text='Statewise Number of people Deceased',
                  plot_bgcolor='rgb(275, 270, 273)')
fig.show()

Analysis of Covid-19 among world

In [51]:
df2 = pd.read_csv('Total_World_covid-19.csv')
df2
Out[51]:
Country Confirmed Deaths Recovered Active Critical Tot Cases/1M pop Deaths/1M pop TotalTests Tests/ 1M pop Population
0 USA 1686436 99300.0 451702.0 1135434.0 17135.0 5098.0 300.00 14749756.0 44587.0 3.308064e+08
1 Brazil 365213 22746.0 149911.0 192556.0 8318.0 1719.0 107.00 735224.0 3461.0 2.124057e+08
2 Russia 344481 3541.0 113299.0 227641.0 2300.0 2361.0 24.00 8685305.0 59518.0 1.459283e+08
3 Spain 282852 28752.0 196958.0 57142.0 854.0 6050.0 615.00 3556567.0 76071.0 4.675300e+07
4 UK 259559 36793.0 NaN NaN 1559.0 3825.0 542.00 3458905.0 50979.0 6.785008e+07
5 Italy 229858 32785.0 140479.0 56594.0 553.0 3801.0 542.00 3447012.0 57003.0 6.047047e+07
6 France 182584 28367.0 64617.0 89600.0 1655.0 2798.0 435.00 1384633.0 21217.0 6.525919e+07
7 Germany 180328 8371.0 160300.0 11657.0 889.0 2153.0 100.00 3595059.0 42922.0 8.375724e+07
8 Turkey 156827 4340.0 118694.0 33793.0 769.0 1862.0 52.00 1832262.0 21749.0 8.424494e+07
9 India 138917 4024.0 57721.0 77172.0 8944.0 101.0 3.00 3033591.0 2200.0 1.378604e+09
10 Iran 135701 7417.0 105801.0 22483.0 2615.0 1618.0 88.00 800519.0 9544.0 8.388027e+07
11 Peru 119959 3456.0 49795.0 66708.0 920.0 3644.0 105.00 820967.0 24936.0 3.292343e+07
12 Canada 84699 6424.0 43985.0 34290.0 502.0 2246.0 170.00 1459288.0 38700.0 3.770819e+07
13 China 82985 4634.0 78268.0 83.0 7.0 58.0 3.00 NaN NaN 1.439324e+09
14 Saudi Arabia 72560 390.0 43520.0 28650.0 372.0 2088.0 11.00 703534.0 20242.0 3.475622e+07
15 Chile 69102 718.0 28148.0 40236.0 1090.0 3618.0 38.00 471758.0 24700.0 1.909937e+07
16 Mexico 68620 7394.0 47424.0 13802.0 378.0 533.0 57.00 219164.0 1702.0 1.287924e+08
17 Belgium 57092 9280.0 15272.0 32540.0 256.0 4928.0 801.00 781284.0 67442.0 1.158456e+07
18 Pakistan 56349 1167.0 17482.0 37700.0 111.0 256.0 5.00 483656.0 2194.0 2.204272e+08
19 Netherlands 45236 5822.0 NaN NaN 223.0 2641.0 340.00 313755.0 18315.0 1.713111e+07
20 Qatar 43714 23.0 9170.0 34521.0 188.0 15200.0 8.00 188143.0 65422.0 2.875845e+06
21 Ecuador 36756 3108.0 3560.0 30088.0 208.0 2087.0 176.00 106079.0 6022.0 1.761463e+07
22 Belarus 36198 199.0 14155.0 21844.0 92.0 3831.0 21.00 450627.0 47687.0 9.449627e+06
23 Bangladesh 33610 480.0 6901.0 26229.0 1.0 204.0 3.00 243583.0 1481.0 1.645199e+08
24 Sweden 33459 3998.0 4971.0 24490.0 249.0 3315.0 396.00 209900.0 20797.0 1.009289e+07
25 Singapore 31616 23.0 14876.0 16717.0 8.0 5408.0 4.00 294414.0 50365.0 5.845641e+06
26 Switzerland 30736 1906.0 28100.0 730.0 50.0 3554.0 220.00 370464.0 42837.0 8.648175e+06
27 Portugal 30623 1316.0 17549.0 11758.0 78.0 3002.0 129.00 689705.0 67621.0 1.019958e+07
28 UAE 29485 245.0 15056.0 14184.0 1.0 2985.0 25.00 1600923.0 162071.0 9.877923e+06
29 Ireland 24639 1608.0 21060.0 1971.0 54.0 4996.0 326.00 295626.0 59940.0 4.932053e+06
30 South Africa 22583 429.0 11100.0 11054.0 128.0 381.0 7.00 583855.0 9857.0 5.923039e+07
31 Indonesia 22271 1372.0 5402.0 15497.0 NaN 82.0 5.00 248555.0 910.0 2.732239e+08
32 Poland 21326 996.0 9194.0 11136.0 160.0 563.0 26.00 767441.0 20276.0 3.785065e+07
33 Kuwait 21302 156.0 6117.0 15029.0 177.0 4996.0 37.00 271089.0 63578.0 4.263883e+06
34 Colombia 21175 727.0 5016.0 15432.0 136.0 417.0 14.00 252742.0 4973.0 5.082667e+07
35 Ukraine 20986 617.0 7108.0 13261.0 256.0 480.0 14.00 285626.0 6527.0 4.375870e+07
36 Romania 18070 1185.0 11399.0 5486.0 188.0 939.0 62.00 368482.0 19142.0 1.924983e+07
37 Egypt 17265 764.0 4807.0 11694.0 41.0 169.0 7.00 135000.0 1322.0 1.021257e+08
38 Israel 16717 279.0 14153.0 2285.0 45.0 1818.0 30.00 537489.0 58438.0 9.197590e+06
39 Japan 16550 820.0 13413.0 2317.0 168.0 131.0 6.00 271201.0 2144.0 1.265138e+08
40 Austria 16503 640.0 15063.0 800.0 29.0 1833.0 71.00 401857.0 44645.0 9.001207e+06
41 Dominican Republic 14801 458.0 8133.0 6210.0 113.0 1366.0 42.00 65355.0 6031.0 1.083667e+07
42 Philippines 14035 868.0 3249.0 9918.0 81.0 128.0 8.00 295593.0 2701.0 1.094278e+08
43 Argentina 12076 452.0 3732.0 7892.0 171.0 267.0 10.00 129418.0 2866.0 4.515311e+07
44 Denmark 11360 562.0 9900.0 898.0 21.0 1962.0 97.00 537742.0 92872.0 5.790165e+06
45 S. Korea 11206 267.0 10226.0 713.0 15.0 219.0 5.00 826437.0 16121.0 5.126484e+07
46 Serbia 11159 238.0 5857.0 5064.0 12.0 1277.0 27.00 217856.0 24924.0 8.740747e+06
47 Panama 10926 306.0 6279.0 4341.0 72.0 2536.0 71.00 58240.0 13520.0 4.307539e+06
48 Afghanistan 10582 218.0 1075.0 9289.0 19.0 273.0 6.00 30052.0 774.0 3.883176e+07
49 Bahrain 9138 14.0 4587.0 4537.0 8.0 5392.0 8.00 276552.0 163192.0 1.694642e+06
50 Czechia 8955 315.0 6078.0 2562.0 29.0 836.0 29.00 399896.0 37349.0 1.070701e+07
51 Kazakhstan 8531 35.0 4352.0 4144.0 31.0 455.0 2.00 671774.0 35822.0 1.875327e+07
52 Norway 8352 235.0 7727.0 390.0 12.0 1542.0 43.00 229769.0 42417.0 5.416911e+06
53 Algeria 8306 600.0 4784.0 2922.0 22.0 190.0 14.00 NaN NaN 4.376577e+07
54 Nigeria 7839 226.0 2263.0 5350.0 7.0 38.0 1.00 44458.0 216.0 2.055702e+08
55 Oman 7770 37.0 1933.0 5800.0 31.0 1526.0 7.00 72000.0 14140.0 5.092104e+06
56 Morocco 7433 199.0 4703.0 2531.0 1.0 202.0 5.00 142882.0 3876.0 3.686493e+07
57 Malaysia 7245 115.0 5945.0 1185.0 9.0 224.0 4.00 507682.0 15707.0 3.232253e+07
58 Australia 7114 102.0 6531.0 481.0 5.0 279.0 4.00 1243157.0 48810.0 2.546906e+07
59 Moldova 7093 250.0 3713.0 3130.0 286.0 1758.0 62.00 40565.0 10054.0 4.034871e+06
60 Ghana 6683 32.0 1998.0 4653.0 16.0 216.0 1.00 193705.0 6248.0 3.100208e+07
61 Armenia 6661 81.0 3064.0 3516.0 10.0 2248.0 27.00 50397.0 17011.0 2.962695e+06
62 Finland 6579 307.0 4800.0 1472.0 19.0 1188.0 55.00 166900.0 30127.0 5.539869e+06
63 Bolivia 6263 250.0 629.0 5384.0 3.0 537.0 21.00 22294.0 1913.0 1.165626e+07
64 Cameroon 4890 165.0 1865.0 2860.0 28.0 185.0 6.00 NaN NaN 2.647218e+07
65 Iraq 4469 160.0 2738.0 1571.0 NaN 111.0 4.00 186885.0 4658.0 4.012309e+07
66 Azerbaijan 4122 49.0 2607.0 1466.0 42.0 407.0 5.00 270739.0 26727.0 1.012978e+07
67 Luxembourg 3992 110.0 3767.0 115.0 7.0 6388.0 176.00 67729.0 108385.0 6.248910e+05
68 Honduras 3950 180.0 468.0 3302.0 13.0 399.0 18.00 14790.0 1496.0 9.887829e+06
69 Sudan 3820 165.0 458.0 3197.0 NaN 87.0 4.00 401.0 9.0 4.373599e+07
70 Hungary 3756 491.0 1711.0 1554.0 28.0 389.0 51.00 164619.0 17037.0 9.662723e+06
71 Guatemala 3424 58.0 258.0 3108.0 5.0 192.0 3.00 31427.0 1758.0 1.787980e+07
72 Guinea 3275 20.0 1673.0 1582.0 24.0 250.0 2.00 14407.0 1100.0 1.309262e+07
73 Uzbekistan 3164 13.0 2565.0 586.0 4.0 95.0 0.40 460000.0 13765.0 3.341792e+07
74 Senegal 3047 35.0 1456.0 1556.0 12.0 183.0 2.00 35016.0 2097.0 1.669436e+07
75 Thailand 3042 57.0 2928.0 57.0 61.0 44.0 0.80 328073.0 4701.0 6.978258e+07
76 Tajikistan 2929 46.0 1301.0 1582.0 NaN 308.0 5.00 NaN NaN 9.514051e+06
77 Greece 2878 171.0 1374.0 1333.0 19.0 276.0 16.00 153963.0 14765.0 1.042792e+07
78 Bulgaria 2427 130.0 840.0 1457.0 29.0 349.0 19.00 74096.0 10656.0 6.953369e+06
79 Bosnia and Herzegovina 2401 144.0 1680.0 577.0 4.0 731.0 44.00 59532.0 18135.0 3.282754e+06
80 Ivory Coast 2376 30.0 1219.0 1127.0 NaN 90.0 1.00 23444.0 891.0 2.630548e+07
81 Djibouti 2270 10.0 1064.0 1196.0 NaN 2301.0 10.00 22097.0 22400.0 9.864800e+05
82 Croatia 2244 99.0 2027.0 118.0 6.0 546.0 24.00 61482.0 14968.0 4.107668e+06
83 DRC 2141 63.0 317.0 1761.0 NaN 24.0 0.70 NaN NaN 8.924918e+07
84 El Salvador 1983 35.0 698.0 1250.0 25.0 306.0 5.00 75146.0 11591.0 6.482911e+06
85 North Macedonia 1978 113.0 1422.0 443.0 21.0 949.0 54.00 25270.0 12129.0 2.083382e+06
86 Cuba 1941 82.0 1689.0 170.0 3.0 171.0 7.00 94060.0 8304.0 1.132729e+07
87 Gabon 1934 12.0 459.0 1463.0 10.0 871.0 5.00 9908.0 4463.0 2.219916e+06
88 Estonia 1823 64.0 1532.0 227.0 1.0 1374.0 48.00 75779.0 57129.0 1.326447e+06
89 Iceland 1804 10.0 1791.0 3.0 NaN 5290.0 29.00 58225.0 170739.0 3.410180e+05
90 Lithuania 1623 63.0 1138.0 422.0 17.0 595.0 23.00 267719.0 98219.0 2.725743e+06
91 Somalia 1594 61.0 204.0 1329.0 2.0 101.0 4.00 NaN NaN 1.584302e+07
92 Mayotte 1587 20.0 894.0 673.0 11.0 5833.0 74.00 5200.0 19112.0 2.720840e+05
93 Slovakia 1509 28.0 1301.0 180.0 1.0 276.0 5.00 158414.0 29017.0 5.459382e+06
94 New Zealand 1504 21.0 1456.0 27.0 1.0 312.0 4.00 261315.0 54235.0 4.818226e+06
95 Slovenia 1468 107.0 1340.0 21.0 4.0 706.0 51.00 75110.0 36130.0 2.078910e+06
96 Kyrgyzstan 1433 16.0 992.0 425.0 5.0 220.0 2.00 100488.0 15430.0 6.512696e+06
97 Maldives 1371 4.0 144.0 1223.0 9.0 2541.0 7.00 11775.0 21825.0 5.395210e+05
98 Kenya 1214 51.0 383.0 780.0 1.0 23.0 1.00 59260.0 1105.0 5.364113e+07
99 Sri Lanka 1141 9.0 674.0 458.0 1.0 53.0 0.40 54834.0 2562.0 2.140425e+07
100 Venezuela 1121 10.0 262.0 849.0 2.0 39.0 0.40 804004.0 28266.0 2.844372e+07
101 Lebanon 1114 26.0 688.0 400.0 4.0 163.0 4.00 74224.0 10870.0 6.828370e+06
102 Guinea-Bissau 1114 6.0 42.0 1066.0 NaN 568.0 3.00 1500.0 764.0 1.962847e+06
103 Hong Kong 1066 4.0 1030.0 32.0 1.0 142.0 0.50 202930.0 27091.0 7.490759e+06
104 Tunisia 1051 48.0 917.0 86.0 3.0 89.0 4.00 47816.0 4050.0 1.180581e+07
105 Latvia 1047 22.0 712.0 313.0 2.0 555.0 12.00 99049.0 52459.0 1.888124e+06
106 Mali 1030 65.0 597.0 368.0 NaN 51.0 3.00 3483.0 173.0 2.018449e+07
107 Albania 998 32.0 789.0 177.0 5.0 347.0 11.00 13279.0 4614.0 2.878103e+06
108 Equatorial Guinea 960 11.0 165.0 784.0 NaN 687.0 8.00 854.0 611.0 1.397634e+06
109 Haiti 958 27.0 22.0 909.0 NaN 84.0 2.00 2718.0 239.0 1.138800e+07
110 Niger 945 61.0 783.0 101.0 NaN 39.0 3.00 5989.0 248.0 2.410319e+07
111 Cyprus 935 17.0 594.0 324.0 10.0 775.0 14.00 99733.0 82666.0 1.206464e+06
112 Costa Rica 930 10.0 620.0 300.0 3.0 183.0 2.00 23378.0 4594.0 5.089334e+06
113 Zambia 920 7.0 336.0 577.0 1.0 50.0 0.40 20011.0 1092.0 1.832563e+07
114 Paraguay 862 11.0 307.0 544.0 NaN 121.0 2.00 24812.0 3483.0 7.123379e+06
115 Burkina Faso 814 52.0 672.0 90.0 NaN 39.0 2.00 NaN NaN 2.083853e+07
116 Uruguay 769 22.0 618.0 129.0 5.0 221.0 6.00 38146.0 10985.0 3.472528e+06
117 Andorra 762 51.0 653.0 58.0 3.0 9864.0 660.00 3750.0 48542.0 7.725300e+04
118 Georgia 730 12.0 522.0 196.0 6.0 183.0 3.00 48565.0 12172.0 3.989910e+06
119 Diamond Princess 712 13.0 651.0 48.0 4.0 NaN NaN NaN NaN 0.000000e+00
120 Jordan 708 9.0 471.0 228.0 5.0 69.0 0.90 163173.0 16009.0 1.019267e+07
121 Sierra Leone 707 40.0 241.0 426.0 NaN 89.0 5.00 NaN NaN 7.959309e+06
122 Chad 675 60.0 215.0 400.0 NaN 41.0 4.00 NaN NaN 1.637228e+07
123 San Marino 665 42.0 266.0 357.0 1.0 19603.0 1238.00 3968.0 116967.0 3.392400e+04
124 South Sudan 655 8.0 6.0 641.0 NaN 59.0 0.70 3356.0 300.0 1.118005e+07
125 Malta 610 6.0 476.0 128.0 1.0 1382.0 14.00 60812.0 137763.0 4.414260e+05
126 CAR 604 1.0 22.0 581.0 NaN 125.0 0.20 11570.0 2400.0 4.820755e+06
127 Nepal 603 3.0 87.0 513.0 NaN 21.0 0.10 138841.0 4774.0 2.908039e+07
128 Ethiopia 582 5.0 152.0 425.0 NaN 5.0 0.04 81010.0 707.0 1.146462e+08
129 Channel Islands 558 45.0 517.0 -4.0 NaN 3212.0 259.00 10255.0 59039.0 1.736980e+05
130 Jamaica 552 9.0 211.0 332.0 1.0 186.0 3.00 10230.0 3456.0 2.959871e+06
131 Madagascar 527 2.0 142.0 383.0 6.0 19.0 0.07 5670.0 205.0 2.761131e+07
132 Tanzania 509 21.0 183.0 305.0 7.0 9.0 0.40 NaN NaN 5.954100e+07
133 Congo 487 16.0 147.0 324.0 NaN 88.0 3.00 NaN NaN 5.502963e+06
134 Réunion 452 1.0 411.0 40.0 2.0 505.0 1.00 17200.0 19225.0 8.946620e+05
135 Taiwan 441 7.0 414.0 20.0 NaN 19.0 0.30 70697.0 2969.0 2.381251e+07
136 Palestine 423 3.0 357.0 63.0 NaN 83.0 0.60 44876.0 8819.0 5.088300e+06
137 Togo 381 12.0 141.0 228.0 NaN 46.0 1.00 16747.0 2028.0 8.257247e+06
138 Cabo Verde 380 3.0 155.0 222.0 NaN 684.0 5.00 1307.0 2353.0 5.553600e+05
139 Isle of Man 336 24.0 303.0 9.0 2.0 3953.0 282.00 4523.0 53219.0 8.498800e+04
140 Mauritius 334 10.0 322.0 2.0 NaN 263.0 8.00 102247.0 80411.0 1.271559e+06
141 French Guiana 328 1.0 145.0 182.0 NaN 1101.0 3.00 NaN NaN 2.978140e+05
142 Rwanda 327 NaN 237.0 90.0 NaN 25.0 NaN 58477.0 4527.0 1.291643e+07
143 Vietnam 325 NaN 267.0 58.0 2.0 3.0 NaN 275000.0 2828.0 9.724858e+07
144 Montenegro 324 9.0 315.0 0.0 NaN 516.0 14.00 10167.0 16188.0 6.280580e+05
145 Nicaragua 279 17.0 199.0 63.0 NaN 42.0 3.00 NaN NaN 6.616331e+06
146 Liberia 265 26.0 139.0 100.0 NaN 53.0 5.00 NaN NaN 5.044519e+06
147 Sao Tome and Principe 251 8.0 4.0 239.0 NaN 1148.0 37.00 175.0 800.0 2.187200e+05
148 Eswatini 250 2.0 156.0 92.0 NaN 216.0 2.00 4994.0 4309.0 1.158921e+06
149 Mauritania 237 6.0 15.0 216.0 NaN 51.0 1.00 2583.0 557.0 4.635933e+06
150 Yemen 222 42.0 10.0 170.0 NaN 7.0 1.00 120.0 4.0 2.975378e+07
151 Myanmar 201 6.0 122.0 73.0 NaN 4.0 0.10 18644.0 343.0 5.437276e+07
152 Uganda 198 NaN 68.0 130.0 NaN 4.0 NaN 84975.0 1865.0 4.557438e+07
153 Martinique 197 14.0 91.0 92.0 NaN 525.0 37.00 NaN NaN 3.752930e+05
154 Mozambique 194 NaN 51.0 143.0 NaN 6.0 NaN 8463.0 272.0 3.115623e+07
155 Benin 191 3.0 82.0 106.0 NaN 16.0 0.20 27954.0 2313.0 1.208756e+07
156 Faeroe Islands 187 NaN 187.0 0.0 NaN 3829.0 NaN 9178.0 187904.0 4.884400e+04
157 Guadeloupe 161 14.0 115.0 32.0 3.0 402.0 35.00 3573.0 8930.0 4.001170e+05
158 Gibraltar 154 NaN 147.0 7.0 NaN 4571.0 NaN 6524.0 193636.0 3.369200e+04
159 Brunei 141 1.0 137.0 3.0 2.0 323.0 2.00 18411.0 42126.0 4.370470e+05
160 Mongolia 141 NaN 33.0 108.0 25.0 43.0 NaN 12407.0 3791.0 3.272661e+06
161 Guyana 135 10.0 62.0 63.0 2.0 172.0 13.00 1457.0 1853.0 7.861700e+05
162 Bermuda 133 9.0 81.0 43.0 2.0 2135.0 144.00 6362.0 102119.0 6.230000e+04
163 Cayman Islands 129 1.0 61.0 67.0 NaN 1965.0 15.00 8426.0 128363.0 6.564200e+04
164 Cambodia 124 NaN 122.0 2.0 1.0 7.0 NaN 16642.0 997.0 1.669458e+07
165 Trinidad and Tobago 116 8.0 108.0 0.0 NaN 83.0 6.00 2930.0 2094.0 1.399036e+06
166 Aruba 101 3.0 95.0 3.0 3.0 946.0 28.00 2070.0 19396.0 1.067210e+05
167 Bahamas 100 11.0 46.0 43.0 1.0 255.0 28.00 1972.0 5020.0 3.928570e+05
168 Monaco 98 4.0 90.0 4.0 2.0 2499.0 102.00 NaN NaN 3.921400e+04
169 Barbados 92 7.0 70.0 15.0 4.0 320.0 24.00 4664.0 16232.0 2.873400e+05
170 Comoros 87 1.0 21.0 65.0 NaN 100.0 1.00 NaN NaN 8.675730e+05
171 Syria 86 4.0 41.0 41.0 NaN 5.0 0.20 NaN NaN 1.745340e+07
172 Malawi 83 4.0 33.0 46.0 1.0 4.0 0.20 2411.0 126.0 1.907456e+07
173 Liechtenstein 82 1.0 55.0 26.0 NaN 2151.0 26.00 900.0 23612.0 3.811700e+04
174 Sint Maarten 77 15.0 59.0 3.0 1.0 1798.0 350.00 438.0 10228.0 4.282500e+04
175 Libya 75 3.0 39.0 33.0 NaN 11.0 0.40 4351.0 634.0 6.861458e+06
176 Angola 69 4.0 18.0 47.0 NaN 2.0 0.10 10000.0 305.0 3.274862e+07
177 French Polynesia 60 NaN 60.0 0.0 NaN 214.0 NaN 3873.0 13795.0 2.807440e+05
178 Zimbabwe 56 4.0 25.0 27.0 NaN 4.0 0.30 37039.0 2496.0 1.484004e+07
179 Macao 45 NaN 45.0 0.0 NaN 69.0 NaN NaN NaN 6.484030e+05
180 Burundi 42 1.0 20.0 21.0 NaN 4.0 0.08 284.0 24.0 1.185030e+07
181 Saint Martin 40 3.0 33.0 4.0 1.0 1036.0 78.00 553.0 14328.0 3.859500e+04
182 Eritrea 39 NaN 39.0 0.0 NaN 11.0 NaN NaN NaN 3.541249e+06
183 Botswana 35 1.0 19.0 15.0 NaN 15.0 0.40 16100.0 6861.0 2.346458e+06
184 Bhutan 27 NaN 6.0 21.0 NaN 35.0 NaN 15342.0 19906.0 7.707260e+05
185 Antigua and Barbuda 25 3.0 19.0 3.0 1.0 256.0 31.00 183.0 1870.0 9.784600e+04
186 Gambia 25 1.0 13.0 11.0 NaN 10.0 0.40 1476.0 613.0 2.408973e+06
187 Timor-Leste 24 NaN 24.0 0.0 NaN 18.0 NaN 738.0 561.0 1.315727e+06
188 Grenada 22 NaN 17.0 5.0 4.0 196.0 NaN 3007.0 26736.0 1.124710e+05
189 Namibia 21 NaN 14.0 7.0 NaN 8.0 NaN 3027.0 1194.0 2.535948e+06
190 Laos 19 NaN 14.0 5.0 NaN 3.0 NaN 5487.0 755.0 7.264396e+06
191 Belize 18 2.0 16.0 0.0 NaN 45.0 5.00 1435.0 3616.0 3.968500e+05
192 St. Vincent Grenadines 18 NaN 14.0 4.0 NaN 162.0 NaN 209.0 1884.0 1.109050e+05
193 New Caledonia 18 NaN 18.0 0.0 NaN 63.0 NaN 5454.0 19122.0 2.852150e+05
194 Fiji 18 NaN 15.0 3.0 NaN 20.0 NaN 1300.0 1451.0 8.957840e+05
195 Saint Lucia 18 NaN 18.0 0.0 NaN 98.0 NaN 898.0 4893.0 1.835430e+05
196 Curaçao 17 1.0 14.0 2.0 NaN 104.0 6.00 504.0 3073.0 1.640260e+05
197 Dominica 16 NaN 16.0 0.0 NaN 222.0 NaN 433.0 6017.0 7.196800e+04
198 Saint Kitts and Nevis 15 NaN 15.0 0.0 NaN 282.0 NaN 391.0 7355.0 5.316100e+04
199 Falkland Islands 13 NaN 13.0 0.0 NaN 3749.0 NaN 445.0 128316.0 3.468000e+03
200 Turks and Caicos 12 1.0 10.0 1.0 NaN 310.0 26.00 126.0 3259.0 3.866200e+04
201 Greenland 12 NaN 11.0 1.0 NaN 211.0 NaN 1885.0 33210.0 5.676000e+04
202 Vatican City 12 NaN 2.0 10.0 NaN 14981.0 NaN NaN NaN 8.010000e+02
203 Seychelles 11 NaN 11.0 0.0 NaN 112.0 NaN NaN NaN 9.828500e+04
204 Suriname 11 1.0 9.0 1.0 NaN 19.0 2.00 488.0 833.0 5.860920e+05
205 Montserrat 11 1.0 10.0 0.0 NaN 2204.0 200.00 36.0 7212.0 4.992000e+03
206 Western Sahara 9 NaN 6.0 3.0 NaN 15.0 NaN NaN NaN 5.957040e+05
207 MS Zaandam 9 2.0 NaN 7.0 NaN NaN NaN NaN NaN 0.000000e+00
208 British Virgin Islands 8 1.0 6.0 1.0 NaN 265.0 33.00 167.0 5528.0 3.021100e+04
209 Papua New Guinea 8 NaN 8.0 0.0 NaN 0.9 NaN 2402.0 269.0 8.928690e+06
210 Caribbean Netherlands 6 NaN 6.0 0.0 NaN 229.0 NaN 424.0 16184.0 2.619800e+04
211 St. Barth 6 NaN 6.0 0.0 NaN 608.0 NaN 137.0 13875.0 9.874000e+03
212 Anguilla 3 NaN 3.0 0.0 NaN 200.0 NaN 30.0 2001.0 1.498900e+04
213 Lesotho 2 NaN NaN 2.0 NaN 0.9 NaN 283.0 132.0 2.140514e+06
214 Saint Pierre Miquelon 1 NaN 1.0 0.0 NaN 173.0 NaN NaN NaN 5.797000e+03
In [52]:
import plotly.graph_objs as go
fig = go.Figure(data=[
go.Bar(name='Confirmed', x=df2['Country'], y=df2['Confirmed'],marker_color='#FF0000'),
go.Bar(name='Recovered', x=df2['Country'], y=df2['Recovered'],marker_color='#2fcc41')])
fig.update_layout(barmode='stack',width=3000, height=600)
fig.update_traces(textposition='inside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.update_layout(title_text='Countrywise Number of people Confirmed and Recovered among them',
                  plot_bgcolor='rgb(275, 270, 273)')
fig.update_layout(plot_bgcolor='rgb(275, 270, 273)',yaxis_title='Total_Confirmed',xaxis_title='Country')
fig.show()
In [ ]:
 
In [53]:
import plotly.graph_objs as go
fig = go.Figure(data=[
go.Bar(name='Confirmed', x=df2['Country'], y=df2['Confirmed'],marker_color='#FF0000'),
go.Bar(name='Deceased', x=df2['Country'], y=df2['Deaths'],marker_color='blue')])
fig.update_layout(barmode='stack',width=3000, height=600)
fig.update_traces(textposition='inside')
fig.update_layout(uniformtext_minsize=8, uniformtext_mode='hide')
fig.update_layout(title_text='Countrywise Number of people Confirmed and Deceased among them',
                  plot_bgcolor='rgb(275, 270, 273)')
fig.update_layout(plot_bgcolor='rgb(275, 270, 273)',yaxis_title='Total_Confirmed',xaxis_title='Country')
fig.show()
In [54]:
px.pie(df2, values='Confirmed', names='Country', color='Population', title='Countrywise Number of people Confirmed')
In [ ]:
 
In [55]:
fig = go.Figure(data=[go.Pie(labels=df2.Country, values=df2.Deaths, hole=.3)])
fig.update_layout(title_text='Countrywise Number of people Deceased',
                  plot_bgcolor='rgb(275, 270, 273)')
fig.show()

Result: In this project we have analyze the data for Covid-19. Also we have applied Machine Learning Algorithm as well as Prophet model to predict the future data. We have done data visualization of present as well as future data. our accuracy of model prediction is 87.98%.

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